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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade 3 Changes in the Climate System on Seasonal to Interannual Timescales SUMMARY Today, we have entered a new age of climate prediction. The past 10 years have witnessed significant advancements in our ability to observe, understand, and predict a year ahead the fundamental dynamics of the El Niño-Southern Oscillation (ENSO) system. We are moving into an era when climate predictions will increasingly affect the prosperity and security of the American people (and people worldwide) through information that will reduce the impacts of destructive natural climate fluctuations, such as droughts, which lead to forest fires and crop failures; floods, which lead to loss of life and obstruction of commerce; and heat and cold waves, which lead to human misery and deprivation. A dedicated community of government and university meteorologists and oceanographers has achieved swift and remarkable progress. We have already begun to predict aspects of El Niño in the tropical Pacific. These forecasts are used by the affected countries (Peru, Brazil, Australia, Chile, Columbia, the Philippines, and the American Flag Pacific Islands) and have helped to increase the prosperity and security of those countries. The actions taken over the next few years will determine whether this predictive capability can be developed for more productive use in the United States. Implementation of the Tropical Oceans and Global Atmosphere program and the Global Energy and Water Cycle Experiment demonstrates the feasibility of end-to-end, multidisciplinary scientific analysis within the U.S. Global Change Research Program (USGCRP). In many ways these efforts are pathfinders for work on the some of the central issues of global change. By virtue of ENSO's timescale, the predictions of these efforts can be rigorously tested against observations. Thus, the capability of making the first genuine dynamic climate predic-
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade tions has been demonstrated, and the capability to use these predictions is being developed. In deploying a dedicated observing system in the tropical Pacific, a coherent observational base was created to test and improve predictive models. Data from this observing system have also been made available in real time over the Internet, demonstrating the possibilities of making data freely available in an environment where data and conclusions have commercial and strategic value. Research Imperatives that must be met to understand climate on seasonal to interannual timescales include the following: ENSO. Maintain and improve the capability to make ENSO predictions. Global monsoon. Define and predict global seasonal to interannual variability, especially the global monsoon systems, and understand the extent to which variability is predictable. Land surface exchanges. Understand the roles of land surface energy and water exchanges and their correct representation in models for seasonal to interannual prediction. Downscaling. Improve the ability to interpret the effects of large-scale climate variability on a local scale. Terrestrial hydrology. Understand the seasonal to interannual factors that influence land surface manifestations of the hydrological cycle, such as floods, droughts, and other extreme weather. INTRODUCTION Understanding climate variability on seasonal to interannual timescales, especially with regard to the hydrological cycle, offers some of the most direct benefits in all of global change research. In particular, better prediction of precipitation is of special interest because it can change the way people interact with the environment, perhaps in revolutionary ways. Precipitation is a fundamental determinant of climate and human habitability through its relationship to land surface conditions, including soil moisture, snow cover, vegetation, evaporation, stream discharge, and surface temperature. An improved capability to model and predict precipitation variability on seasonal to interannual timescales is therefore of potentially great socioeconomic benefit for water and energy resource management, agriculture, and a variety of other factors related to general human well-being. In this chapter, case studies highlight recent applications of seasonal to interannual climate prediction, in particular the prediction of precipitation, in geographical areas from Asia to Brazil to the central United States. These cases indicate the promise of climate forecasting and also the issues that such forecasting raises in particular applications. Some important advances have come from the study of ocean-atmosphere interactions. Some aspects of ENSO are predictable a year or more in advance.1 This predictive capability for ENSO must not only be maintained and improved
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade but expanded to areas where major benefits can be obtained, notably in exploring the predictability of monsoon systems of North America and Australia-Asia. Several prominent streams of climate research deal with continental processes—on how land surface properties affect the overlying atmosphere and how climate effects are manifested on land surfaces. Such interactions involving land surfaces offer the potential for applications at local scales, applications that may be the most relevant to people. The slowly changing boundary conditions of the land surface in midlatitudes have also prompted the notion that predictability may lie in the “memory” of such systems. Research has also emphasized the seasonal to interannual factors that influence floods, droughts, and other climate extremes, which drive much of the interest in climate. The human dimensions of seasonal to interannual climate research are a recurring theme throughout this chapter. The case study in northeast Brazil highlights the significance of climate prediction capability for an agrarian society. Learning to apply seasonal to interannual climate predictions well, for the benefit of human society, is an important research imperative, but it is not directly addressed here. The topic is covered more extensively in Chapter 7. CASE STUDIES 1982 to 1983 El Niño Inspires Real-Time Measurements By the early 1980s, much interest had arisen regarding the phenomenon of El Niño, an enhancement of the normally warm oceanic current appearing off the coast of Peru around Christmas time. The El Niño of 1972 to 1973 had coincided with the decline of the Peruvian anchoveta industry, which had supplied 20 percent (by weight) of the world 's fish catch. Since anchoveta was a major global source of cattle feed, the precipitous decline of this industry drove up world soybean prices and focused the world's attention on a phenomenon that had previously been assumed to be local to the South American coast. There was only fragmentary evidence in the 1970s about the causes and mechanisms of El Niño, but there was enough to suggest that El Niño consisted of a large-scale, coupled atmosphere-ocean phenomenon occurring primarily over the wide expanses of the equatorial Pacific, that it had local manifestations near the coasts of Peru and Ecuador, and that it was connected to the interannual variations of east-west pressure gradients called the Southern Oscillation. From that time forward, El Niño was identified as the warm phase of ENSO. Oceanographic measurements in the equatorial Pacific during the 1960s and 1970s had given oceanographers a set of rules about the sequencing and onset properties of ENSO's warm phases. On the basis of these rules, an El Niño watch was established to “catch” an El Niño when one was about to occur. The rules indicated that one was due in 1975, and many ships were launched to observe the
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade El Niño, but warm conditions in the tropical Pacific failed to appear (a warm phase of ENSO did occur in 1976). By the early 1980s, papers had appeared that more fully explained the sequence of events during warm and cold ENSO phases and the influence of these warm and cold sea surface temperatures (SSTs) on the rest of the northern hemisphere climate.2 These important advances, together with establishment of the World Climate Research Program to study the role of the ocean in climate, set the stage for a major international study of ENSO that was eventually to become the Tropical Oceans and Global Atmosphere (TOGA) program. It was Bjerknes's insight that ENSO was an inherently atmosphere-ocean phenomenon that required both meteorologists and oceanographers to cooperate in planning TOGA. The full wisdom of this approach became clear only later when it was realized that the underlying mechanism for ENSO would never have been discovered by meteorologists or oceanographers separately working within their own disciplines. U.S. scientists met in October 1982 at Princeton University to plan the program. There was much discussion about then-current events in the tropical Pacific. But old ideas and the paucity of data misled everyone. Some even argued vehemently that El Niño could not occur at the same time that the largest warm phase of ENSO in this century (at the time) was already present in the tropical Pacific. In fact, satellite measurements of SST had been rendered ambiguous by the simultaneous eruption of El Chichón, and no other credible in situ network was in place to ground truth the satellite measurements. Several months later it became clear that the largest ENSO warm phase of the century had taken place largely out of view of the world' s scientists, an event leading to the realization that real-time measurements of the tropical Pacific were essential. This experience, in conjunction with the development of a dynamical predictive capability for SST in the tropical Pacific,3 eventually led to the new TAO (Tropical Atmosphere-Ocean) array of 70 moorings in the tropical Pacific. TAO telemeters back the data on winds and upper-ocean thermal structure to the Global Telecommunication System, from which it is available to all.a A 1996 report provides a complete history and extensive references. 4 Macroscale Climate Variability and Crop Yields in the Monsoon Regions The annual cycle of the monsoon systems has led the inhabitants of monsoon regions to divide their lives, customs, and economies into two quite different phases: the “wet” and the “dry.” The wet phase refers to the rainy season, during which warm, moist, and very disturbed winds blow inland from warm tropical a A picture of conditions for the tropical Pacific yesterday is available at http://www.pmel.noaa.gov/toga-tao/realtime.html. All prediction web sites are given at http://www.atmos.washington.edu/tpop/pop.htm. Various predictions of conditions in the tropical Pacific are provided for next year.
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade oceans. The dry phase refers to the other half of the year, whenwinds bring cool, dry air from wintering continents. This distinctive variation of the annual cycle occurs over Asia, Australia, western Africa, and the Americas. In some locations (e.g., the Asia-Australia sector) the dry winter air flows across the equator, picking up moisture from the warm tropical oceans to become the wet monsoon of the summering continent. In this manner the “dry” of the winter monsoon is tied to the “wet” of the summer monsoon and vice versa. In contrast, regions closer to the equator have two rainy seasons. For example, in equatorial East Africa the two rainy seasons occur in March to May and September to December and fall between the two African monsoon circulations. These are referred to as the “long” and “short” rains, respectively. Agrarian-based societies have developed in the monsoon regions because of the abundant solar radiation and precipitation, the two ingredients for successful agriculture. Agricultural practices have traditionally been tied strictly to the annual cycle. Whereas the regularity of the warm and moist and the cool and dry phases of the monsoon would seem to be ideal for agricultural societies, their regularity makes agriculture very susceptible to small changes in the annual cycle. Small variations in the timing and quantity of rainfall can have significant societal consequences. A weak monsoon year (i.e., with significantly less total rainfall than normal) generally corresponds to low crop yields. A strong monsoon usually produces abundant crops, although too much rainfall may produce devastating floods. In addition to the importance of the strength of the overall monsoon in a particular year, forecasting the onset of the subseasonal variability (e.g., the active periods and the lulls or breaks in between) is of particular importance. A late- or early-onset monsoon or an ill-timed lull in the monsoon rains may have very serious consequences for agriculture, even when mean annual rainfall is normal. As a result, forecasting monsoon variability on timescales ranging from weeks to years is an issue of considerable urgency. An example of Indian rice yield susceptibility to monsoon variations is provided to illustrate these points. Figure 3.1a plots rice production in India between 1960 and 1996. Figure 3.1b plots the All-India Rainfall Index (AIRI).5 AIRI is a measure of total summer rainfall over India. The relationship between crop yield and AIRI was first noted in 1988.6 Figure 3.1a and Figure 3.1b provide an updated version of this relationship. In general, rice production has increased linearly during the past few decades. Superimposed on this trend are variations in crop production of about 15 to 20 percent. Some periods of production deficit are associated with El Niño years in the Pacific Ocean (shaded bars), while some abundant years are associated with La Niña, or “cold” events in the Pacific (diagonal bars). Figure 3.2a is a scatter plot of the AIRI and the crop yield as functions of their percent deviations from the mean. The correlation between the two time series is +0.61. All El Niño years (black triangles) fall in the negative quadrant, while all La Niña years (black squares) lie in the positive quadrant. Finally, the relationship between the preceding winter Southern Oscillation Index
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade FIGURE 3.1 Relationship of Indian rice production to Indian rainfall. Production in 1978 = 100. SOURCE: Webster et al. (1998); adapted from Gadgil (1995). Courtesy of the American Geophysical Union. (SOI)—that is, the pressure difference between Tahiti and Darwin, Australia 7—and the AIRI, is plotted in Figure 3.2b. Generally, warm events in the tropics are associated with deficient rainfall, while cold events appear to be related to abundant rainfall. The relationship between ENSO conditions and the Indian rice yield suggests a number of questions: Although the relationship between ENSO conditions and Indian rice yield is not perfect, it is regular enough to raise the tantalizing suggestion that macroscale variations in the climate system influence variability on the
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade FIGURE 3.2 Scatterplots of (a) AIRI and crop production relationship, depending on their values' percent deviation from the mean, and (b) relationship between AIRI and preceding winter SOI index. (See text for further explanation.) SOURCE: Webster et al. (1998). Courtesy of the American Geophysical Union.
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade smaller scale of India and South Asia. How is this connection manifested in the physical system? Do irregularities in the ENSO/crop yield relationship indicate that intra-seasonal rainfall variability (e.g., the timing of the onset and first break of the monsoon in a particular summer, relative to plowing, planting, and harvesting) also influences total crop yield? Do the irregularities in the relationship between SOI and AIRI suggest inherent limitations in their linkage? What are the factors involved in any such limitations? How accurate must a seasonal forecast of monsoon rainfall be to be of use to the user community? How far in advance would a forecast have to be made? In the preceding discussion, Indian crop yield is used as an example of the importance of discerning the ways that macroscale climate variability affects the local scale. The questions raised above are common to the monsoon regions of Australia, Africa, and the Americas. Snow-Monsoon Interactions In an effort to predict monsoons over a century ago, it was speculated that the varying extent and thickness of Himalayan snow exert some influence on the climatic conditions and weather over the plains of northwest India.8 Himalayan snow was therefore assessed via snowfall reports from various locations in the western Himalayan range as one of the predictors of Indian monsoon rainfall.9 Greater winter snowfall was found to be related to below-normal monsoon rainfall for the period 1880 to 1920. However, for the subsequent 30-year period, snowfall was highly variable and its relationship with the monsoon was reversed. Its use as a predictor was dropped. Since the early 1970s, the Advanced Very High Resolution Radiometer (AVHRR) aboard National Oceanographic and Atmospheric Administration (NOAA) satellites has provided a snow cover dataset that is sufficiently accurate for continental-scale studies. Some pioneering work10 examined the snow-monsoon relationship using these satellite data. Several observational studies, some examining the role of Eurasian snow extent, others focusing on the Himalayan snow, suggested an inverse snow-monsoon relationship—that is, the less the snowfall, the greater the monsoon. In the northern hemisphere snow cover ranges from 7 to over 40 percent of the total land area, making it the most rapidly changing natural surface. Snow cover and snow depth in a particular season can be related to atmospheric circulation of the next season through a series of feedback mechanisms. The two main physical processes through which snow anomalies may affect climate on a seasonal timescale are the albedo effect and the hydrological ef-
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade fect. Excessive snow in the early part of winter tends to reduce solar radiation in winter (up to four times compared to bare ground) by increasing the surface albedo, thus resulting in the persistence of colder temperatures (and possibly additional snow anomalies). Thus, holding other processes constant, excess snowfall gives rise to a positive feedback. In particular, positive snow anomalies over the Eurasian continent in winter and spring lead to colder ground temperatures in the following summer and hence anomalously weak meridional temperature gradients, because a substantial fraction of the solar energy available in spring and early summer would go to melting the snow and evaporating water from the wet soil. This lower land-ocean temperature contrast would presumably lead to below-normal monsoon. The entire scenario would be reversed when winter and spring Eurasian snows are below normal precipitation. General circulation modeling sensitivity experiments substantiate observational evidence of an inverse snow-monsoon relationship.12 In analyzing the relative role of SST variations and land surface processes on the interannual variability of the Asian monsoon system, it is recognized that the former plays a dominant role. The quasibiennial aspect of monsoons has been investigated, and it has been noted that monsoons play an active role in determining the anomalous state of the warm-water pool in the western Pacific in the following autumn and winter seasons.13 Studies have also suggested an intriguing three-way interaction between Eurasian snow cover, monsoon, and ENSO.14 Forecasting Seasonal to Interannual Variations in Northeast Brazil The northeastern part of Brazil (in particular, the state of Cear á) is semiarid, has a rainy season from February to April, and is subject to wide rainfall fluctuations from year to year. Throughout Brazilian history, severely dry periods have been marked by severe social dislocations and mass migrations, which have affected the 30 million people of Ceará and the entire social and economic fabric of Brazilian culture. Statistical correlations of rainfall with climatic indices15 have indicated that Ceará's rainfall is correlated with SST in both the Atlantic and the eastern Pacific. Realizing the vulnerability of its economy to such interannual climate fluctuations, the state of Ceará, in conjunction with the federal government, established an institute called FUNCEME (Funda ção Cearense de Meteorologia e Recursos Hídricos—Ceará's Foundation for Meteorology and Hydrological Resources) to advise the state on the proper actions to take in anticipation of adverse climatic conditions. FUNCEME has published a monthly information bulletin (Monitor Climático) since 1987 that gives monthly global climatic data, ENSO predictions, and local precipitation and hydrological data. FUNCEME maintains programs addressing both long- and short-term is-
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade sues. For the long term it advises on actions to be taken on water resources and distribution, well recovery, crop choices and distribution, soil conditions, and environmental degradation. For the short term it issues forecasts for the rainy season and explicit instructions to the various regions of Ceará about the timing of planting and the crops to emphasize, depending on the forecast of abundant or deficient rainfall. As a result of these activities, the agricultural output of Ceará has gradually grown more stable, no longer subject to the drastic ups and downs of interannual climatic variability.16 For example, the normal grain output for normal rainfall years in Ceará is 540,000 metric tons. In 1987, before concerted action policies were in place, the response to a poor rainfall year (30 percent below normal) was that grain production for that year was 260,000 metric tons, which led to severe hardship in Ceará and the need for relief by the central governmental. In 1992, however, while the rainfall was equally poor (27 percent below normal), a set of actions in response to a relatively accurate forecast allowed the grain production to be 430,000 tons (see Figure 3.3). Even with a second consecutive very poor rainfall year (again relatively accurately forecast), grain production was 190,000 tons. Land Surface Factors and Climate Prediction at Less Than Interannual Timescales: The 1993 Mississippi Floods Terrestrial hydrological-atmospheric coupling processes are reasonably well understood at local scales, and there is increasing understanding on a regional basis. Observational studies and model experiments both suggest that terrestrial hydrological-atmospheric coupling cannot be fully rationalized in terms of local context because the integrated effect of land surface modifies the air prior to its arrival at a specific location.17 At the regional scale, some studies suggest that the type of climatic moisture regime prevalent at the beginning of the warm season may be significantly correlated with the subsequent evolution of both temperature and humidity.18 Early warm season conditions, most likely related to global circulation, can provide the land surface with either more or less moisture relative to the long-term mean. This anomaly may influence subsequent moisture conditions, either locally or regionally, as larger-scale atmospheric circulation becomes less important and local convection (and perhaps moisture recycling) becomes more important in the summer season. Preliminary coupled terrestrial hydrological-atmospheric modeling studies19 tend to support this hypothesis. Modeling experiments carried out in the context of the Global Energy Water-Cycle Experiment Continental-Scale International Project (GCIP) —specifically using the Atmospheric Model Inter-Comparison Project 's 10-year runs—show that it is possible to provide improved simulation of the mean annual cycle when soil moisture is specified. In practice the soil moisture values used in these runs are estimated from observed temperature and precipitation, rather than from true
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade FIGURE 3.3 Grain production (1,000 tons) verses precipitation (millimeters) in Ceará, Northeast Brazil, for 1987 and 1992, both El Niño years. SOURCE: Based on Moura (1994). Data provided by FUNCEME and IBGE/GCEA. Courtesy of the World Meteorological Organization. soil moisture measurements. Nonetheless, these AMIP simulations suggest that improved specification of soil moisture, and, it might therefore be presumed, improved prediction of the seasonal evolution of soil moisture in coupled terrestrial hydrological-atmospheric models, have the potential to improve seasonal precipitation forecasts. Dynamical processes controlled by regional water and energy balance can influence vapor flow and in this way may contribute to the occurrence of extreme events. The control of land surface processes primarily arises through their effect on the Bowen ratio, which influences the diurnal evolution of the boundary layer. Land surface processes also have substantial influence on elevated mixed layers and on associated “lids” on atmospheric instability that focus the release of convective instability and hence determine the distribution of regional precipitation in time and space.20
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade TABLE 3.1 State and External (or Forcing) Variables that Must Be Measured for the GOALS Program Realm State Variables External Variables Ocean Upper-ocean temperature. Upper-ocean currents. Sea level. Upper-ocean salinity. Optical absorption. Sea ice extent, concentration, and thickness. Wind stress. Net surface solar radiation. Downwelling long-wave radiation. Surface air temperature. Surface humidity. Precipitation. Atmosphere Wind structure. Thermal structure. Surface air temperature. Sea-level pressure. Water vapor structure. Columnar water vapor and liquid water content. Cloud cover and height. Sea surface temperature. Net radiation at top of the atmosphere. Land surface variables (see below). Land Soil moisture. Snow cover and depth. Vegetation type, biomass, and vigor Water runoff. Ground temperature. Precipitation. Net surface long-wave and short-wave radiation. Surface wind. Surface air temperature. Surface humidity. Evaporation. Evapotranspiration. SOURCE: NRC (1998). spheric boundary layer locally, compared to interactions in remote locations, largely determines the occurrence of low clouds, low-level convergence of wind patterns, and deep convection. These fluxes in turn depend heavily on precipitation and solar radiation from the atmosphere. Both the portioning of solar radiation and precipitation at the surface and the amount and ratio of evaporative and sensible fluxes depend on seasonally varying land cover properties related to vegetation. The land surface is highly heterogeneous with regard to these properties, so representing land surface only in terms of properties at the scale of atmospheric models is problematic and of questionable accuracy. The processes responsible for evapotranspiration are now relatively well understood as a point measure. However, they depend in part on details of soil moisture distribution. These details are largely unmeasured and poorly understood at the scales of climate models. Two particular factors determining soil
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade moisture levels are especially poorly understood with regard to representation in climate models: how to represent runoff removal from land surface, and what is and what determines maximum water-holding capacity of the land surface. Much of the continental surface outside the tropics has seasonal snow cover and, in some places, frozen soils, which modify the surface hydrological cycle and solar energy absorption. Snow acts as a water reservoir for months or more but can at times release copious amounts of water rapidly. Greater understanding of land surface exchanges and their representation in seasonal to interannual prediction models will require a combination of process studies, model evaluation exercises, and model experiments. Much of the required activity can be undertaken effectively through the set of continental-scale experiments being fostered by the World Climate Research Programme. Among these experiments, the Global Energy Water-Cycle Experiment Continental-Scale International Project (GCIP) in the United States and the Large-Scale Biosphere Atmosphere Experiment in South America are of particular relevance to the seasonal to interannual component of the USGCRP. However, the knowledge and model improvement provided by these last two continental-scaie experiments must be effectively merged with the results of ocean-atmosphere studies by the GOALS program, to create a coherent U.S. seasonal to interannual prediction program that focuses on the Americas. Downscaling Imperative GCMs are currently the primary tools for studies of climate variability and change. Because of computational considerations relating to nonlinear dynamic equations and the current capabilities of supercomputers, GCM global grids have horizontal resolutions on the order of 100 to 250 km. Unfortunately, information generated at that scale (uniformly applied over the grid) is inadequate to describe many small-scale features of importance to the hydrological and energy cycles. A good example concerning atmospheric aspects of the hydrological cycle is the role of clouds. Moist warm air is transported upward in narrow, cloud-scale regions (kilometers), inducing compensating subsidence of cooler air at large scales (hundreds of kilometers). An example related to land surface hydrology is the way that surface and subsurface runoff production is greatly influenced by the spatial and temporal heterogeneity of precipitation inputs, the heterogeneity of soil hydraulic characteristics and flow pathways, and the heterogeneity of antecedent conditions induced by downslope flows and controlled by topography. While various LSPs have been developed (and are continuing to be developed) to model small- to large-scale interactions, further work is required on the methodologies to desegregate regional model outputs at the scales of hydrological processes. Both LSPs and hydrological models have many parameters that are tuned to the scale of the model. For example, a GCM LSP would use different parameter values than an LSP developed to work at catchment scale with local
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade historical station data. Current GCMs simulate precipitation with reduced intensity and increased frequency.40 Developing LSPs and hydrological models that will work with a variety of global and regional observational data would be useful for many studies. A stochastic precipitation disaggregation scheme has been developed that redistributes the GCM-calculated grid average precipitation into a subgrid scale to improve the modeled interaction of the atmosphere and land surface.41 The authors demonstrate that this approach provides a more realistic partitioning of precipitation into snow and rain (judged on the basis of ground-based observations) and a spatial distribution that better conforms to the actual observed heterogeneity of rainfall patterns for the areas (of GCM grid size) that were tested. The latter point is particularly important, considering that, for instance, the total annual runoff volume generated in the Colorado River basin is due to precipitation falling over about 10 percent of the total surface area. The importance of the GCM-generated grid average precipitation relating to the region of actual interest is clear, with regard to water resource management issues at local and regional scales. In addition to the above-mentioned downscaling concerns, complex modeling issues abound in runoff production. For example, the mechanism through which water is partitioned into runoff and infiltration when snow and ice melt is a critical unresolved issue. Most LSPs follow the lead of the prior generation of bucket models in assuming that runoff either instantly vanishes or in a few cases goes directly to the ocean. Flow-routing schemes are required to overcome this deficiency. The GCIP program is attempting to address some of these downscaling issues. Additionally, there is great need to understand the scale dependence of the inputs and outputs as well as how to adjust the scales to give the best possible predictions, so that our capability to interpret the effects of large-scale climate variability on regional and local scales can be improved. Terrestrial Hydrology Research Imperative Variations in climate at seasonal to interannual timescales have important implications for terrestrial hydrology. Although precipitation is the most important determinant of key terrestrial hydrological variables (streamflow, soil moisture, snowpack water content, and evapotranspiration), the land surface acts as a low-pass nonlinear filter, so that antecedent precipitation as well as current precipitation play important roles in smoothing the effects of climate variability at these timescales. The implications of land surface processing of precipitation at these timescales are not well understood. For instance, recent work in the Appalachicola-Chattahoochee-Flint (ACF) river system of the southeastern United States suggests that, even though a fairly strong ENSO signal can be detected in precipitation, the signal appears to be filtered out in the streamflow
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade record, where it is much weaker. On the other hand, in the Columbia River basin, a strong ENSO signal is present in the streamflow record. Although it may be surmised that the links between winter precipitation (which is largely stored as snowpack) and spring-summer streamflow are likely to be more direct in the Columbia than in the ACF, the effects of land surface processing in seasonal to interannual signals in precipitation are still not fully understood. These effects are further complicated by temperature, which affects both rain-snow transition and evapotranspiration. For example, evapotranspiration mitigates the effects on streamflow of warm wet periods (especially in summer) and exacerbates the effects of cold wet conditions. Especially in the western United States, cold wet winters result in much larger snowpacks, and hence greater spring and summer runoff, than do warm wet winters. For hydrological purposes, seasonal to interannual climate forecasts could have at least two important functions. The first is to forecast the evolution of hydrologically important surface variables (especially precipitation and temperature) using methods such as ensemble forecasting to represent the range of future conditions over the forecast period. There are mechanisms to extend current methods, such as extended streamflow prediction, to use ensemble forecasts of the time series of surface variables. Important research issues in developing this approach have to do with resolution of biases in the climate forecasts (especially for precipitation). Seasonal to interannual forecasts might also be used in a probabilistic (or risk) context. For instance, seasonal to interannual climatic variability might be reflected in changes in the 100-year flood in ENSO versus non-ENSO years. How best to achieve such forecasts remains to be determined, but the general strategy would be based on classification of the current condition and/or forecasts of the evolution of future conditions at the regional scale, combined with retrospective frequency analysis. Observational Requirements: Land Surface Exchanges, Downscaling, and Terrestrial Hydrology Research Imperatives The primarily hydrological observational datasets needed in support of the above imperatives can be described as follows. Streamflow The U.S. Geological Survey should take steps to ensure the long-term viability of the stream gages in the Hydro-Climatic Data Network, particularly those having at least 50 years of continuous record. Currently, most of these stations are funded cooperatively for a variety of operational purposes and are difficult to sustain for long-term research purposes.
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade Precipitation Station Data The United States must maintain existing long-term stations within the National Climatic Data Center (NCDC) cooperative network, particularly the subset of those stations that make up the Historical Climate Network. Radar Precipitation Data The National Center for Environmental Prediction has recently started to archive a merged WSR88-D (Doppler radar)/gauge product (4-km resolution) that covers most of the United States. The suitability of these data for climatological purposes needs to be evaluated, and steps must be taken to ensure the security of the long-term archive of these data and that they are freely available to the scientific community. Surface Radiation Only a very small number of stations now operate in the continental United States that collect a full suite of surface radiation observations (the SURFRAD network). The adequacy of this network for studies of seasonal to interannual variability should be evaluated. Snow Point Observations Snow water equivalent point observations are collected primarily at Natural Resources Conservation Service Snow Telemetry sites in mountainous areas of the western United States. The suitability of these sites for long-term climate studies needs to be evaluated (the longest records from these stations date only to the mid-1980s). Snow depth measurements are collected at some NCDC cooperative stations. The feasibility of using some subset of these stations to measure snow water equivalent should be evaluated. The object is to achieve a much more uniform spatial distribution of station-based observations. Areal Extent NOAA's National Operational Hydrologic Remote Sensing Center and its National Environmental Satellite, Data, and Information Service (among other entities) produce satellite-based snow areal extent measurements of the conti-
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade nental United States and the world. These products are currently used for operational purposes but could prove extremely valuable for assessing such interactions as those between the continental extent of seasonal snow cover and largearea circulation patterns. Steps should be taken to ensure that these data are climatologically useful. Alogrithums Satellite remote sensing algorithms to estimate snow water equivalent in a manner suitable for global studies (e.g., spatial resolutions of tens of kilometers) have been improved and may be suitable for seasonal to interannual timescale studies, notwithstanding problems remaining for forested areas. These products need to be assessed and archived at (or through) the National Snow and Ice Data Center. Wind and Humidity Although these wind and humidity data are collected at NCDC Surface Airways Stations, many of the records are affected by station and instrument changes, and their use for climatological purposes is problematic. Nonetheless, they are critical for computing potential evapotranspiration and provide reference values for approaches that can lead to spatial estimates (e.g., modeling combined with atmospheric profile data or analysis fields). Thus, more attention should be given to the climatological value of these observations. Surface Air and Skin Temperature Observations of surface air temperature are critically important to predict evapotranspiration and snow accumulation and melt. Surface air temperature measurements are routinely collected at NCDC cooperative observer stations as well as at National Weather Service manned observing stations. Because air temperature tends to have much higher spatial correlations locally than, for instance, precipitation, maintenance of an adequate station precipitation network should assure adequacy of surface air temperature measurements, provided that these variables are coincidentally collected. Direct observations of surface (skin) temperature are much more problematic. Surface observations have generally only been collected in research projects and are difficult to interpret. Nonetheless, skin temperature is a state variable predicted by most land surface schemes (some schemes predict an effective vegetation temperature as well). Thus, these measurements might be updated as well. Satellite sensors and algorithms can produce global estimates of skin temperature at time frequencies as high as daily; a ground network could play a critical role in
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade validating and calibrating the long-term spatial records that are now being acquired. Surface Energy Fluxes Direct observations of surface energy fluxes (latent heat, sensible heat, and ground heat flux) have hitherto mainly been collected in conjunction with short-term research programs (e.g., the First ISLSCP Field Experiment and the Hydrological Atmospheric Pilot Experiments). However, recent advances in flux measuring instrumentation (see Chapter 2) indicate that their routine, long-term operation is now becoming feasible. A commitment has been made, for example, to continue long-term operation of some of the BOREAS tower flux sites. The instruments required for routine measurement of surface energy fluxes are a subset of those required to make the more difficult measurements of carbon dioxide for ecosystem research. However, in the case of seasonal to interannual studies, there is an additional need for near-real-time data provision. In the course of the next decade, a network of surface energy and carbon dioxide flux measurement sites should be established to characterize seasonal to interannual changes in the surface-atmosphere exchanges for major vegetation types in the United States and perhaps globally. Soil Moisture Soil moisture plays a key role in partitioning net radiation into latent, sensible, and ground heat fluxes, particularly in summer. Some studies have indicated the potential importance of feedbacks between soil moisture and climate, especially in the interior of the northern hemisphere continents in summer. Therefore, observation of soil moisture, through ground- or satellite-based observing systems, or both, is of great importance. A few networks in the continental United States collect ground-based point observations of soil moisture, including networks of the Illinois Water Survey and the Oklahoma Mesonet. While there are questions about how point observations of soil moisture can be interpreted in the context of small-scale spatial variability and about the lack of standard instruments, the feasibility of ground-based networks needs to be evaluated. With regard to remote sensing, both active and passive microwave sensors have shown potential in estimating near-surface soil moisture. Furthermore, in combination with modeling, these surface observations might be extended to greater depths. Many issues remain, and problems of soil moisture estimation from satellite sensors are themselves the subject of ongoing research. Nonetheless, passive microwave sensors have demonstrated the capability to map spatial and temporal soil moisture variability at the watershed scale. With a carefully designed sensor system and carefully formulated algorithms, such measurements can be made
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade from space at the global scale.42 Viable proposals for candidate satellite missions have been developed and should be given careful attention and support. Vegetation Knowledge of seasonal and interannual variability in vegetation properties is critical to understanding links between the land surface and climate at seasonal to interannual timescales. Satellite-based estimates of vegetation properties, such as leaf area index and greenness, are now fairly widely used in numerical weather prediction models. Earth Observing System-era developments (e.g., the moderate-resolution imaging spectrometer) will almost certainly improve the quality of these products. A long-term global archive of seasonal variations in vegetation properties must be preserved, along with sufficient metadata to resolve questions about any effects of changes in instruments. NOTES 1. NRC (1996). 2. ENSO phases, in particular, Rasmusson and Carpenter (1983); northern hemisphere climate, Horel and Wallace (1981). 3. Cane et al. (1986). 4. NRC (1996). The report describes the TAO array and the other remote and in situ measurement systems providing continuously available data in the tropical Pacific, referred to elsewhere in this report as the ENSO Observing System. 5. Adapted from Mooley and Parthasarathy (1984). 6. Parthasarathy et al. (1988). 7. E.g., Trenberth (1997). 8. Blanford (1984). 9. Walker and Bliss (1932). 10. Hahn and Shukla (1976). 11. Shukla (1987). 12. E.g., Barnett et al. (1989), Vernekar et al. (1995). 13. Meehl (1987), Yasunari (1990), Yasunari and Seki (1992). 14. E.g., Khandekar (1991), Meehl (1994), Yang (1996). 15. Hastenrath (1990a, b). 16. Moura (1994). 17. Charney (1975), Charney et al. (1977). 18. E.g., Rind (1982), Mintz (1984), Delworth and Manabe (1989). 19. Koster and Suarez (1995). 20. Benjamin and Carlson (1986), Clark and Arritt (1995). 21. Modeling, McCorcle (1988), Paegle et al. (1996), Beljaars et al. (1996), Betts et al. (1996); observations, Mitchell et al. (1995). 22. Rasmusson (1967). 23. Betts et al. (1993). 24. Paegle et al. (1996). 25. Beljaars et al. (1996). 26. Seth and Giorgi (1998).
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade 27. Cane et al. (1986). 28. Charney et al. (1950). 29. D. Chen et al. (1995, 1997). 30. E.g., Y.Q. Chen et al. (1995). 31. D. Chen et al. (1995, 1997). 32. Zhang et al. (1996). 33. A review is given by Lau (1997). 34. Betts et al. (1993). 35. As discussed by Avissar and Pielke (1989). 36. Busalacchi et al. (1981, 1983). 37. See NRC (1994) for a thorough discussion. 38. NRC (1998). 39. Ibid. 40. Gao et al. (1996). 41. Gao and Sorooshian (1994). 42. Journal of Hydrology (1996). REFERENCES AND BIBLIOGRAPHY Avissar, R., and R.A. Pielke. 1989. A parameterization of heterogeneous land surface for atmospheric numerical models and its impact on regional meteorology. Monthly Weather Review 117:2113-2136. Barnett, T.P., L. Dumenil, U. Schlese, E. Roeckner, and M. Latif. 1989. The effect of Eurasian snow cover on regional and global climate variations. Journal of Atmospheric Sciences 46:661-685. Battisti, D.S., and E. Sarachik. 1995. Understanding and predicting ENSO. Reviews of Geophysics 33:1367-1376. Beljaars, A.C.M., P. Viterbo, M.J. Miller, and A.K. Betts. 1996. Anomalous rainfall over the US during July 1993: Sensitivity to land surface parameterization. Monthly Weather Review 124:364-383. Benjamin, S.G., and T.N. Carlson. 1986. Some effects of surface heating and topography in the regional severe storm environment. Part I: Three-dimensional simulations. Monthly Weather Review 114:307-329. Betts, A.K., J.H. Ball, and A.C.M. Beljaars. 1993. Comparison between the land surface response of the European Centre model and the FIFE-1987 data. Quarterly Journal of the Royal Meteorological Society 119:975-1002. Betts, A.K., J.H. Ball, A.C.M. Beljaars, M.J. Miller, and P. Viterbo. 1996. The land-surface-atmosphere interaction: A review based on observational and global modeling perspectives. Journal of Geophysical Reviews 101(D3):7209-7225. Blanford, H.F. 1984. On the connection of the Himalayan snowfall with dry winds and seasons of droughts in India. Proceedings of the Royal Society of London 37:3-22. Busalacchi, A.J., and J.J. O'Brien. 1981. Interannual variability of the equatorial Pacific in the 1960s. Journal of Geophysical Research 86(10):901-10,907. Busalacchi, A.J., K. Takeuchi, and J.J. O'Brien. 1983. Interannual variability of the equatorial Pacific revisited. Journal of Geophysical Research 88:7551-7562. Cane, M.A., S.E. Zebiak, and S.C. Dolan. 1986. Experimental forecasts of El Niño. Nature 321:827-832. Charney, J.G. 1975. Dynamics of desert and drought in the Sahel. Quarterly Journal of the Royal Meteorological Society 101:193-202. Charney, J.G., R. Fjörtoft, and J. von Neumann. 1950. Numerical integration of the barotropic voracity equation. Tellus 2:237-254.
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade Charney, J.G., W.J. Quirk, S.H. Chow, and J. Kornfield. 1977. A comparative study of the effects of albedo change on drought in semi-arid regions. Journal of Atmospheric Sciences 34:1366-1385. Chen, D., S.E. Zebiak, A.J. Busalacchi, and M.A. Cane. 1995. An improved procedure for El Niño forecasting. Science 269:1699-1702. Chen, D., S.E. Zebiak, M.A. Cane, and A.J. Busalacchi. 1997. Initialization and predictability of a coupled ENSO forecast model Monthly Weather Review 125:773-788. Chen, Y.Q., D.S. Battisti, T.N. Palmer, J. Barsugli, and E.S. Sarachik. 1995. A study of the predictability of tropical Pacific SST in a coupled atmosphere/ocean model using singular vector analysis: The role of the annual cycle and the ENSO cycle. Monthly Weather Review 125:831-845. Clark, C.A., and R.W. Arritt. 1995. Numerical simulations of the effect of soil moisture and vegetation cover on the development of deep convection. Journal of Applied Meteorology 34:2029-2045. Delworth, T., and S. Manabe. 1989. The influence of soil wetness on near-surface atmospheric variability Journal of Climate 2(12):1447-1462. Gao, X., and S. Sorooshian. 1994. A stochastic precipitation disaggregation scheme for GCM applications Journal of Climatology 7(2):238-247. Gao, X., S. Sorooshian, and H.V. Gupta. 1996. Sensitivity analysis of the biosphere-atmosphere transfer scheme. Journal of Geophysical Research 101(D3):7279-7289. Hahn, D.G., and J. Shukla. 1976. An apparent relationship between the Eurasian snow cover and Indian monsoon rainfall. Journal of Atmospheric Sciences 33:2461-2462. Hastenrath, S. 1990a. Prediction of Northeast Brazil rainfall anomalies. Journal of Climate 3:893-904. Hastenrath, S. 1990b. Tropical climate prediction—a progress report, 1985-90. Bulletin of the American Meteorological Society 71(N6):819-825. Horel, J.D., and J.M. Wallace. 1981. Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Monthly Weather Review 109:813-829. Journal of Hydrology. 1996. Special Issue on Remote Sensing of Soil Moisture. Journal of Hydrology 184:1-2. Khandekar, M.L. 1991. Eurasian snow cover, Indian monsoon and El Niño Southern Oscillation—a synthesis. Atmosphere-Ocean 29:636-647. Koster, R.D., and M.J. Suarez, 1992. Modeling the land surface boundary condition in climate models as a composite of independent vegetation stands. Journal of Geophysical Research 97:2697-2716. Koster, R.D., and M.J. Suarez. 1995. Relative contributions of land and ocean processes to precipitation variability. Journal of Geophysical Research 100(D7):13775-137900. Lau, N.C. 1997. Interactions between global SST anomalies and the midlatitude atmospheric circulation. Bulletin of the American Meteorological Society 78:21-33. Manabe, S., J. Smagorinsky, and R.F. Strickler. 1965. Simulated climatology of a general circulation model with a hydrological cycle. Monthly Weather Review 93:769-798. Meehl, G.A. 1987. The annual cycle and interannual variability in the tropical Pacific and Indian Ocean region. Monthly Weather Review 115:27-50. Meehl, G.A. 1994. Coupled land-ocean-atmosphere processes and south Asian monsoon variability Science 266:263-266. Mintz, Y. 1984. The sensitivity of numerically simulated climates to land-surface boundary conditions. Pp. 79-105 in The Global Climate,J. Houghton, ed. Cambridge University Press, Cambridge, U.K. Mitchell, M.J., R.W. Arritt, and K. Labas. 1995. A climatology of the warm season Great Plains low-level jet using wind profiler observations. Weather and Forecasting 10:576-591.
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GLOBAL ENVIRONMENTAL CHANGE: Research Pathways for the Next Decade McCorcle, M.D. 1988. Simulation of surface moisture effects on the Great Plains low-level jet. Monthly Weather Review 116:1705-1720. Mooley, D.A., and B Parthasarathy. 1984. Indian summer monsoon and El Niño. Pure and Applied Geophysics 121(2):339-352. Moura, A.D., 1994. Prospects for seasonal-to-interannual climate prediction and applications for sustainable development. Bulletin of the World Meteorological Organization 43(3):207-215. National Research Council (NRC). 1994. Ocean-Atmosphere Observations Supporting Short-Term Climate Predictions. National Academy Press, Washington, D.C. National Research Council (NRC). 1996. Learning to Predict El Niño: Accomplishments and Legacies of the TOGA Program. National Academy Press, Washington, D.C. National Research Council (NRC). 1998. A Scientific Strategy for U.S. Participation in CLIVAR/GOALS. National Academy Press, Washington, D.C. Paegle, J., K.C. Mo, and J. Nogues-Paegle. 1996. Dependence of simulated precipitation on surface evaporation during the 1993 United States summer floods. Monthly Weather Review 124:345-361. Parthasarathy, B., A.A. Munot, and D.R. Kothawale. 1988. Regression model for estimation of Indian foodgrain production from summer monsoon rainfall. Agricultural and Forest Meteorology 42(2-3):167-182. Rasmusson, E.M. 1967. Atmospheric water vapor transport and the water balance of North America. Part I. Characteristics of the water vapor flux field. Monthly Weather Review 95:403-425. Rasmusson, E.M., and T.H. Carpenter. 1983. The relationship between eastern equatorial Pacific sea surface temperature and rainfall over India and Sri Lanka. Monthly Weather Review 111:517-528. Rind, D. 1982. The influence of ground moisture conditions in North America on summer climate as modeled in the GISS GCM. Monthly Weather Review 110:1487-1494. Seth, A., and F. Giorgi. 1998. The effects of domain choice on summer precipitation simulation and sensitivity in a regional climate model. Journal of Climatology 11(10):2698-2713. Shukla, J. 1987. Long-range forecasting of monsoons. Pp. 339-547 in Monsoons,J.S. Fein and P.L. Stephens, eds. John Wiley &Sons, New York. Trenberth, K.E. 1997. Short-term climate variations: Recent accomplishments and issues for future progress. Bulletin of the American Meteorological Society 78(6):1081-1096. Vernekar, A.D., J. Zhou, and J. Shukla. 1995. The effect of Eurasian snow cover on the Indian monsoon. Journal of Climatology 8:248-266. Walker, G.T., and E.W. Bliss. 1932. World weather V. Memoirs of the Royal Meteorological Society 4:53-84. Webster, P.J., V.O. Magana, T.N. Palmer, J. Shukla, R.A. Thomas, M. Yanai, and T. Yasunari. 1998. The Monsoon: Processes, Predictability and the Prospects for Prediction Journal of Geophysical Research v. 103 no C7 14,451-14,510 June 29, 1998. Yang, S. 1996. ENSO-snow-monsoon associations and seasonal-interannual predictions International Journal of Climatology 16:125-134. Yasunari, T. 1990. Impact of the Indian monsoon on the coupled atmosphere-ocean system in the tropical Pacific. Meteorology and Atmospheric Physics 44:29-41. Yasunari, T., and Y. Seki. 1992. Role of the Asian monsoon on the interannual variability of the global climate system. Journal of the Meteorological Society of Japan 70:177-189. Zhang, Y., J.M. Wallace, and D.S. Battisti. 1996. ENSO-like decade-to-century scale variability 1900-93. Journal of Climate 10:1004-1020.
Representative terms from entire chapter: