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Data from 15 models of phase 5 of the Coupled Model Intercomparison Project (CMIP5) for preindustrial, historical, and future climate change experiments are examined for consensus changes in land surface variables, fluxes, and metrics relevant to land-atmosphere interactions. Consensus changes in soil moisture and latent heat fluxes for past-to-present and present-to-future periods are consistent with CMIP3 simulations, showing a general drying trend over land (less soil moisture, less evaporation) over most of the globe, with the notable exception of high northern latitudes during winter. Sensible heat flux and net radiation declined from preindustrial times to current conditions according to the multimodel consensus, mainly due to increasing aerosols, but that trend reverses abruptly in the future projection. No broad trends are found in soil moisture memory except for reductions during boreal winter associated with high-latitude warming and diminution of frozen soils. Land-atmosphere coupling is projected to increase in the future across most of the globe, meaning a greater control by soil moisture variations on surface fluxes and the lower troposphere. There is also a strong consensus for a deepening atmospheric boundary layer and diminished gradients across the entrainment zone at the top of the boundary layer, indicating that the land surface feedback on the atmosphere should become stronger both in absolute terms and relative to the influence of the conditions of the free atmosphere. Coupled with the trend toward greater hydrologic extremes such as severe droughts, the land surface seems likely to play a greater role in amplifying both extremes and trends in climate on subseasonal and longer time scales. [ABSTRACT FROM AUTHOR]

A process-based, semianalytic prototype model for understanding large-scale land-atmosphere coupling is developed here. The metric for quantifying the coupling is the sensitivity of precipitation P to soil moisture W, . For a range of prototype parameters typical of conditions found over tropical or summertime continents, the sensitivity measure exhibits a broad minimum at intermediate soil moisture values. This minimum is attributed to a trade-off between evaporation (or evapotranspiration) E and large-scale moisture convergence across the range of soil moisture states. For water-limited, low soil moisture conditions, is dominated by evaporative sensitivity , reflecting high potential evaporation Ep arising from relatively warm surface conditions and a moisture-deficient atmospheric column under dry surface conditions. By contrast, under high soil moisture (or energy limited) conditions, becomes slightly negative as Ep decreases. However, because convergence and precipitation increase strongly with decreasing (drying) moisture advection, while soil moisture slowly saturates, is large. Variation of key parameters is shown to impact the magnitude of , for example, increasing the time scale for deep convective adjustment lowers at a given W, especially on the moist side of the profile where convergence dominates. While the prototype's applicability for direct quantitative comparison with either observations or models is clearly limited, it nonetheless demonstrates how the complex interplay of surface turbulent and column radiative fluxes, deep convection, and horizontal and vertical moisture transport influences the coupling of the land surface and atmosphere that may be expected to occur in either more realistic models or observations. [ABSTRACT FROM AUTHOR]

This study examines the sensitivity of the global climate to land surface processes (LSP) using an atmospheric general circulation model both uncoupled (with prescribed SSTs) and coupled to an oceanic general circulation model. The emphasis is on the interactive soil moisture and vegetation biophysical processes, which have first-order influence on the surface energy and water budgets. The sensitivity to those processes is represented by the differences between model simulations, in which two land surface schemes are considered: 1) a simple land scheme that specifies surface albedo and soil moisture availability and 2) the Simplified Simple Biosphere Model (SSiB), which allows for consideration of interactive soil moisture and vegetation biophysical process. Observational datasets are also employed to assess the extent to which results are realistic. The mean state sensitivity to different LSP is stronger in the coupled mode, especially in the tropical Pacific. Furthermore, the seasonal cycle of SSTs in the equatorial Pacific, as well as the ENSO frequency, amplitude, and locking to the seasonal cycle of SSTs, is significantly modified and more realistic with SSiB. This outstanding sensitivity of the atmosphere-ocean system develops through changes in the intensity of equatorial Pacific trades modified by convection over land. The results further demonstrate that the direct impact of land-atmosphere interactions on the tropical climate is modified by feedbacks associated with perturbed oceanic conditions ('indirect effect' of LSP). The magnitude of such an indirect effect is strong enough to suggest that comprehensive studies on the importance of LSP on the global climate have to be made in a system that allows for atmosphere-ocean interactions. [ABSTRACT FROM AUTHOR]

Positive soil moisture-precipitation feedbacks can intensify heat and prolong drought under conditions of precipitation deficit. Adequate representation of these processes in regional climate models is, therefore, important for extended weather forecasts, seasonal drought analysis, and downscaled climate change projections. This paper presents the first application of the NASA Unified Weather Research and Forecasting Model (NU-WRF) to simulation of seasonal drought. Simulations of the 2006 southern Great Plains drought performed with and without soil moisture memory indicate that local soil moisture feedbacks had the potential to concentrate precipitation in wet areas relative to dry areas in summer drought months. Introduction of a simple dynamic surface albedo scheme that models albedo as a function of soil moisture intensified the simulated feedback pattern at local scale-dry, brighter areas received even less precipitation while wet, whereas darker areas received more-but did not significantly change the total amount of precipitation simulated across the drought-affected region. This soil-moisture-mediated albedo land-atmosphere coupling pathway is structurally excluded from standard versions of WRF. [ABSTRACT FROM AUTHOR]

The atmospheric moisture budget and surface interactions for the southern Great Plains are evaluated for contrasting May-June periods (1998, 2002, 2006, and 2007) as background for the Cloud and Land Surface Interaction Campaign (CLASIC) of (wet) 7-30 June 2007. Budget components [flux divergence (MFD), storage change (dPW), and inflow (IF/ A)] are estimated from North American Regional Reanalysis data. Precipitation ( P) is calculated from NCEP daily gridded data, evapotranspiration ( E) is obtained as moisture budget equation residual, and the recycling ratio ( PE/ P) is estimated using a new equation. Regional averages are presented for months and five daily P categories. Monthly budget results show that E and E − P are strongly positively related to P; E − P generally is positive and balanced by positive MFD that results from its horizontal velocity divergence component (HD, positive) exceeding its horizontal advection component (HA, negative). An exception is 2007 (CLASIC), when E − P and MFD are negative and supported primarily by negative HA. These overall monthly results characterize low P days (≤0.6 mm), including for nonanomalous 2007, but weaken as daily P approaches 4 mm. In contrast, for 4 < P ≤ 8 mm day−1 E − P and MFD are moderately negative and balanced largely by negative HD except in 2007 (negative HA). This overall pattern was accentuated (including for nonanomalous 2007) when daily P > 8 mm. Daily P E/ P ratios are small and of limited range, with P category averages 0.15-0.19. Ratios for 2007 are above average only for daily P ≤ 4 mm. CLASIC wetness principally resulted from distinctive MFD characteristics. Solar radiation, soil moisture, and crop status/yield information document surface interactions. [ABSTRACT FROM AUTHOR]

This study examines the land-atmosphere coupling strength during summer over subregions of the United States based on observations [Climate Prediction Center (CPC)-Variable Infiltration Capacity (VIC)], reanalysis data [North American Regional Reanalysis (NARR) and NCEP Climate Forecast System Reanalysis (CFSR)], and models [Community Atmosphere Model, version 3 (CAM3)-Community Land Model, version 3 (CLM3) and CAM4-CLM4]. The probability density function of conditioned correlation between soil moisture and subsequent precipitation or surface temperature during the years of large precipitation anomalies is used as a measure for the coupling strength. There are three major findings: 1) among the eight subregions (classified by land cover types), the transition zone Great Plains (and, to a lesser extent, the Midwest and Southeast) are identified as hot spots for strong land-atmosphere coupling; 2) soil moisture-precipitation coupling is weaker than soil moisture-surface temperature coupling; and 3) the coupling strength is stronger in observational and reanalysis products than in the models examined, especially in CAM4-CLM4. The conditioned correlation analysis also indicates that the coupling strength in CAM4-CLM4 is weaker than in CAM3-CLM3, which is further supported by Global Land-Atmosphere Coupling Experiments1 (GLACE1)-type experiments and attributed to changes in CAM rather than modifications in CLM. Contrary to suggestions in previous studies, CAM-CLM models do not seem to overestimate the land-atmosphere coupling strength. [ABSTRACT FROM AUTHOR]

Anthropogenic activities have been significantly perturbing global freshwater flows and groundwater reserves. Despite numerous advances in the development of land surface models (LSMs) and global terrestrial hydrological models (GHMs), relatively few studies have attempted to simulate the impacts of anthropogenic activities on the terrestrial water cycle using the framework of LSMs. From the comparison of simulated terrestrial water storage with the Gravity Recovery and Climate Experiment (GRACE) satellite observations it is found that a process-based LSM, the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO), outperforms the bucket-model-based GHM called H08 in simulating hydrologic variables, particularly in water-limited regions. Therefore, the water regulation modules of H08 are incorporated into MATSIRO. Further, a new irrigation scheme based on the soil moisture deficit is developed. Incorporation of anthropogenic water regulation modules significantly improves river discharge simulation in the heavily regulated global river basins. Simulated irrigation water withdrawal for the year 2000 (2462 km3 yr−1) agrees well with the estimates provided by the Food and Agriculture Organization (FAO). Results indicate that irrigation changes surface energy balance, causing a maximum increase of ~50 W m−2 in latent heat flux averaged over June-August. Moreover, unsustainable anthropogenic water use in 2000 is estimated to be ~450 km3 yr−1, which corresponds well with documented records of groundwater overdraft, representing an encouraging improvement over the previous modeling studies. Globally, unsustainable water use accounts for ~40%% of blue water used for irrigation. The representation of anthropogenic activities in MATSIRO makes the model a suitable tool for assessing potential anthropogenic impacts on global water resources and hydrology. [ABSTRACT FROM AUTHOR]

Land surface model experiments are used to quantify, for a number of U.S. river basins, the contributions (isolated and combined) of soil moisture and snowpack initialization to the skill of seasonal streamflow forecasts at multiple leads and for different start dates. Snow initialization has a major impact on skill during the spring melting season. Soil moisture initialization has a smaller but still statistically significant impact during this season, and in other seasons, its contribution to skill dominates. Realistic soil moisture initialization can contribute to skill at long leads (over 6 months) for certain basins and seasons. Skill levels in all seasons are found to be related to the ratio of initial total water storage (soil water plus snow) variance to the forecast period precipitation variance, allowing estimates of the potential for skill in areas outside the verification basins. [ABSTRACT FROM AUTHOR]

A revised framework for the analysis of soil moisture memory characteristics of climate models and observational data is derived from the approach proposed by Koster and Suarez. The resulting equation allows the expression of the month-to-month soil moisture autocorrelation as a function of 1) the initial soil moisture variability, 2) the (atmospheric) forcing variability over the considered time period, 3) the correlation between initial soil moisture and subsequent forcing, 4) the sensitivity of evaporation to soil moisture, and 5) the sensitivity of runoff to soil moisture. A specific new feature is the disentangling of the roles of initial soil moisture variability and forcing variability, which were both (for the latter indirectly) contributing to the seasonality term of the original formulation. In addition, a version of the framework entirely based on explicit equations for the underlying relationships (i.e., independent of soil moisture statistics at the following time step) is proposed. The validity of the derived equation is exemplified with atmospheric general circulation model (AGCM) simulations from the Global Land-Atmosphere Coupling Experiment (GLACE). [ABSTRACT FROM AUTHOR]

Linkages between the terrestrial ecosystem and precipitation play a critical role in regulating regional weather and climate. These linkages can manifest themselves as positive or negative feedback loops, which may either favor or inhibit the triggering and intensity of thunderstorms. Although the Canadian Prairies terrestrial system has been identified as having the potential to exert a detectable influence on convective precipitation during the warm season, little work has been done in this area using in situ observations. The authors present findings from a novel study designed to explore linkages between the normalized difference vegetation index (NDVI) and lightning duration (DUR) from the Canadian Lightning Detection Network for 38 census agricultural regions (CARs) on the Canadian Prairies. Statistics Canada divides the prairie agricultural zone into CARs (polygons of varying size and shape) for the purpose of calculating agricultural statistics. Here, DUR is used as a proxy for thunderstorm activity. Statistical analyses were undertaken for 38 CARs for summers [June--August (JJA)] between 1999 and 2008. Specifically, coefficients of determination were calculated between pairs of standardized anomalies of DUR and NDVI by season and by month. Correlations were also calculated for CARs grouped by size and/or magnitude of the NDVI anomalies. The main findings are as follows: 1) JJA lightning activity is overwhelmingly below average within larger dry areas (i.e., areas with below-average NDVI); that is, the linkages between NDVI and DUR increased significantly as both the area and magnitude of the dry anomaly increased. 2) In contrast, CARs with above-average NDVI did not consistently experience above-average lightning activity, regardless of the CAR size. 3) The lower threshold for the length scale of the dry anomalies required to affect the boundary layer sufficiently to reduce lightning activity was found to be approximately 150 km (~18 000 km2). 4) The authors'' analysis suggests that the surface-convection feedback appears to be a real phenomenon, in which drought tends to perpetuate drought with respect to convective storms and associated rainfall, within the limits found in 1) and 3). [ABSTRACT FROM AUTHOR]

Rising atmospheric CO2 concentration CO2 and climate change are expected to have a major effect on terrestrial ecosystem hydrological cycles and plant water stress in the coming decades. The present study investigates the potential responses of terrestrial ecosystem hydrological cycles and plant water stress across China to elevated CO2 and climate change in the twentieth and twenty-first centuries using the calibrated and validated Lund--Potsdam--Jena dynamic global vegetation model (LPJ-DGVM) and eight climate change scenarios. The spatiotemporal change patterns of estimated evapotranspiration (ET), soil moisture, runoff, and plant water stress due to climate change and elevated CO2 are plotted singly and in combination. Positive future trends in ET, soil moisture, and runoff--although differing greatly among regions--are projected. Resultant plant water stress over China''s terrestrial ecosystem generally could be eased substantially through the twenty-first century under the climate scenarios driven by emission scenarios that consider economic concerns. By contrast, under the climate scenarios driven by emission scenarios that consider environmental concerns, plant water stress could be eased until 2060, then begin to fluctuate until 2100. The net impact of physiological and structural vegetation responses to elevated CO2 could result in an increasing trend in runoff in southern and northeastern China, and a decreasing trend in runoff in northern and northwestern China in the twentieth century. It is projected to reduce ET by 1.5 ×× 109 to 6.5 ×× 109 m3 yr−−1 on average, and increase runoff by 1.0 ×× 109 to 5.4 ×× 109 m3 yr−−1 during 2001--2100 across China''s terrestrial ecosystems, although the spatial change pattern could be quite diverse. These findings, in partial contradiction to previous results, present an improved understanding of transient responses of China''s terrestrial ecosystem hydrological cycles and plant water stress to climate change and elevated CO2 in the twentieth and twenty-first centuries. [ABSTRACT FROM AUTHOR]

The authors simulated the effects of Amazonian mesoscale deforestation in the boundary layer and in rainfall with the Brazilian Regional Atmospheric Modeling System (BRAMS) model. They found that both the area and shape (with respect to wind incidence) of deforestation and the soil moisture status contributed to the state of the atmosphere during the time scale of several weeks, with distinguishable patterns of temperature, humidity, and rainfall. Deforestation resulted in the development of a three-dimensional thermal cell, the so-called deforestation breeze, slightly shifted downwind to large-scale circulation. The boundary layer was warmer and drier above 1000-m height and was slightly wetter up to 2000-m height. Soil wetness affected the circulation energetics proportionally to the soil dryness (for soil wetness below ∼∼0.6). The shape of the deforestation controlled the impact on rainfall. The horizontal strips lined up with the prevailing wind showed a dominant increase in rainfall, significant up to about 60 000 km2. On the other hand, in the patches aligned in the opposite direction (north--south), there was both increase and decrease in precipitation in two distinct regions, as a result of clearly separated upward and downward branches, which caused the precipitation to increase for patches up to 15 000 km2. The authors'' estimates for the size of deforestation impacting the rainfall contributed to fill up the low spatial resolution in other previous studies. [ABSTRACT FROM AUTHOR]

Changes in the area of Australia experiencing concurrent temperature and rainfall extremes are investigated through the use of two combined indices. The indices describe variations between the fraction of land area experiencing extreme cold and dry or hot and wet conditions. There is a high level of agreement between the variations and trends of the indices from 1957 to 2008 when computed using (i) a spatially complete gridded dataset without rigorous quality control checks and (ii) spatially incomplete high-quality station datasets with rigorous quality control checks. Australian extremes are examined starting from 1911, which is the first time a broad-scale assessment of Australian temperature extremes has been performed prior to 1957. Over the whole country, the results show an increase in the extent of hot and wet extremes and a decrease in the extent of cold and dry extremes annually and during all seasons from 1911 to 2008 at a rate of between 1%% and 2%% decade−−1. These trends mostly stem from changes in tropical regions during summer and spring. There are relationships between the extent of extreme maximum temperatures, precipitation, and soil moisture on interannual and decadal time scales that are similar to the relationships exhibited by variations of the means. However, the trends from 1911 to 2008 and from 1957 to 2008 are not consistent with these relationships, providing evidence that the processes causing the interannual variations and those causing the longer-term trends are different. [ABSTRACT FROM AUTHOR]

Recent research indicates that the warming of the climate system resulting from increased greenhouse gas (GHG) emissions over the next century will persist for many centuries after the cessation of these emissions, principally because of the persistence of elevated atmospheric carbon dioxide (CO2) concentrations and their attendant radiative forcing. However, it is unknown whether the responses of other components of the climate system—including those related to Greenland and Antarctic ice cover, the Atlantic thermohaline circulation, the West African monsoon, and ecosystem and human welfare—would be reversed even if atmospheric CO2 concentrations were to recover to 1990 levels. Here, using a simple set of experiments employing a current-generation numerical climate model, the authors examine the response of the physical climate system to decreasing CO2 concentrations following an initial increase. Results indicate that many characteristics of the climate system, including global temperatures, precipitation, soil moisture, and sea ice, recover as CO2 concentrations decrease. However, other components of the Earth system may still exhibit nonlinear hysteresis. In these experiments, for instance, increases in stratospheric water vapor, which initially result from increased CO2 concentrations, remain present even as CO2 concentrations recover. These results suggest that identification of additional threshold behaviors in response to human-induced global climate change should focus on subcomponents of the full Earth system, including cryosphere, biosphere, and chemistry. [ABSTRACT FROM AUTHOR]

The Global Land–Atmosphere Coupling Experiment (GLACE) built a framework to estimate the strength of the land–atmosphere interaction across many weather and climate models. Within this framework, GLACE-type experiments are performed with a single atmospheric model coupled to three different land models. The precipitation time series is decomposed into three frequency bands to investigate the large-scale connection between external forcing, precipitation variability and predictability, and land–atmosphere coupling strength. It is found that coupling to different land models or prescribing subsurface soil moisture does not change the global pattern of precipitation predictability and variability too much. However, the regional impact of soil moisture can be highlighted by calculating the land–atmosphere coupling strength, which shows very different patterns for the three models. The estimated precipitation predictability and land–atmosphere coupling strength is mainly associated with the low-frequency component of precipitation (periods beyond 3 weeks). Based on these findings, the land–atmosphere coupling strength is conceptually decomposed into the impact of low-frequency external forcing and the impact of soil moisture. Because most models participating in GLACE have overestimated the low-frequency component of precipitation, a calibration to the GLACE-estimated land–atmosphere coupling strength is performed. The calibrated coupling strength is generally weaker, but the global pattern does not change much. This study provides an important clarification of land–atmosphere coupling strength and increases the understanding of the land–atmosphere interaction. [ABSTRACT FROM AUTHOR]

The probabilistic modal response of vegetation to stochastic precipitation variability is studied in a conceptual climate–ecosystem model. It is found that vegetation can exhibit bimodality in a monostable climate–ecosystem under strong rainfall variability and with soil moisture memory comparable with that of the vegetation. The bimodality of vegetation is generated by a convolution of a nonlinear vegetation response and a colored stochastic noise. The nonlinear vegetation response is such that vegetation becomes insensitive to precipitation variability near either end state (green or desert), providing the potential for two preferred modes. The long memory of soil moisture allows the vegetation to respond to a slow stochastic forcing such that the vegetation tends to grow toward its equilibrium states. The implication of the noise-induced bimodality to abrupt changes in the climate–ecosystem is also discussed. [ABSTRACT FROM AUTHOR]

Understanding the occurrence and variability of drought events in historic and projected future climate is essential to managing natural resources and setting policy. The Midwest region is a key contributor in corn and soybean production, and the occurrence of droughts may affect both quantity and quality of these crops. Soil moisture observations play an essential role in understanding the severity and persistence of drought. Considering the scarcity of the long-term soil moisture datasets, soil moisture observations in Illinois have been one of the best datasets for studies of soil moisture. In the present study, the authors use the existing observational dataset and then reconstruct long-term historic time series (1916–2007) of soil moisture data using a land surface model to study the effects of historic climate variability and projected future climate change on regional-scale (Illinois and Indiana) drought. The objectives of this study are to (i) estimate changes and trends associated with climate variables in historic climate variability (1916–2007) and in projected future climate change (2009–99) and (ii) identify regional-scale droughts and associated severity, areal extent, and temporal extent under historic and projected future climate using reconstructed soil moisture data and gridded climatology for the period 1916–2007 using the Variable Infiltration Capacity (VIC) model. The authors reconstructed the soil moisture for a long-term (1916–2007) historic time series using the VIC model, which was calibrated for monthly streamflow and soil moisture at eight U.S. Geological Survey (USGS) gauge stations and Illinois Climate Network’s (ICN) soil moisture stations, respectively, and then it was evaluated for soil moisture, persistence of soil moisture, and soil temperature and heat fluxes. After calibration and evaluation, the VIC model was implemented for historic (1916–2007) and projected future climate (2009–99) periods across the study domain. The nonparametric Mann–Kendall test was used to estimate trends using the gridded climatology of precipitation and air temperature variables. Trends were also estimated for annual anomalies of soil moisture variables, snow water equivalent, and total runoff using a long-term time series of the historic period. Results indicate that precipitation, minimum air temperature, total column soil moisture, and runoff have experienced upward trends, whereas maximum air temperature, frozen soil moisture, and snow water equivalent experienced downward trends. Furthermore, the decreasing trends were significant for the frozen soil moisture in the study domain. The results demonstrate that retrospective drought periods and their severity were reconstructed using model-simulated data. Results also indicate that the study region is experiencing reduced extreme and exceptional droughts with lesser areal extent in recent decades. [ABSTRACT FROM AUTHOR]

Understanding the long-term (interannual–decadal) variability of water availability in river basins is paramount for water resources management. Here, the authors analyze time series of simulated terrestrial water storage components, observed precipitation, and discharge spanning 74 yr in the Colorado River basin and relate them to climate indices that describe variability of sea surface temperature and sea level pressure in the tropical and extratropical Pacific. El Niño–Southern Oscillation (ENSO) indices in winter [January–March (JFM)] are related to winter precipitation as well as to soil moisture and discharge in the lower Colorado River basin. The low-frequency mode of the Pacific decadal oscillation (PDO) appears to be strongly correlated with deep soil moisture. During the negative PDO phase, saturated storage anomalies tend to be negative and the “amplitudes” (mean absolute anomalies) of shallow soil moisture, snow, and discharge are slightly lower compared to periods of positive PDO phases. Predicting interannual variability, therefore, strongly depends on the capability of predicting PDO regime shifts. If indeed a shift to a cool PDO phase occurred in the mid-1990s, as data suggest, the current dry conditions in the Colorado River basin may persist. [ABSTRACT FROM AUTHOR]

Climate model simulations consistently show that surface temperature over land increases more rapidly than over sea in response to greenhouse gas forcing. The enhanced warming over land is not simply a transient effect caused by the land–sea contrast in heat capacities, since it is also present in equilibrium conditions. This paper elucidates the transient adjustment processes over time scales of days to weeks of the surface and tropospheric climate in response to a doubling of CO2 and to changes in sea surface temperature (SST), imposed separately and together, using ensembles of experiments with an atmospheric general circulation model. These adjustment processes can be grouped into three stages: immediate response of the troposphere and surface processes (day 1), fast adjustment of surface processes (days 2–5), and adjustment of the whole troposphere (days 6–20). Some land surface warming in response to doubled CO2 (with unchanged SSTs) occurs immediately because of increased downward longwave radiation. Increased CO2 also leads to reduced plant stomatal resistance and hence restricted evaporation, which increases land surface warming in the first day. Rapid reductions in cloud amount lead in the next few days to increased downward shortwave radiation and further warming, which spreads upward from the surface, and by day 5 the surface and tropospheric response is statistically consistent with the equilibrium value. Land surface warming in response to imposed SST change (with unchanged CO2) is slower. Tropospheric warming is advected inland from the sea, and over land it occurs at all levels together rather than spreading upward from the surface. The atmospheric response to prescribed SST change in about 20 days is statistically consistent with the equilibrium value, and the warming is largest in the upper troposphere over both land and sea. The land surface warming involves reduction of cloud cover and increased downward shortwave radiation, as in the experiment with CO2 change, but in this case it is due to the restriction of moisture supply to the land (indicated by reduced soil moisture), whereas in the CO2 forcing experiment it is due to restricted evaporation despite increased moisture supply (indicated by increased soil moisture). The warming over land in response to SST change is greater than over the sea and is the dominant contribution to the land–sea warming contrast under enhanced CO2 forcing. [ABSTRACT FROM AUTHOR]

While most projections of climate change and its regional impacts focus on overall changes in the state of the climate system, useful information can also be found in the evolution of the climate system from one state to another. Here the authors introduce one method for identifying regions in which significant and systematic long-term nonlinear evolutions may be present, even given quasi-linear anthropogenic forcing. Using climate change projections taken from simulations of NCAR’s Community Climate System Model, version 3 (CCSM3), the authors then employ the technique to isolate systematic nonlinear behavior of soil moisture variations over the United States. While the projections presented here only represent the results from one model system, it is argued that such nonlinear behavior is an important characteristic of future climate change that should be considered when discussing both short-term and long-term impacts of anthropogenic climate forcing. [ABSTRACT FROM AUTHOR]

The uncertainty in the projection of future drought occurrence was explored for four different drought indices using two model ensembles. The first ensemble expresses uncertainty in the parameter space of the third Hadley Centre climate model, and the second is a multimodel ensemble that additionally expresses structural uncertainty in the climate modeling process. The standardized precipitation index (SPI), the precipitation and potential evaporation anomaly (PPEA), the Palmer drought severity index (PDSI), and the soil moisture anomaly (SMA) were derived for both a single CO2 (1×CO2) and a double CO2 (2×CO2) climate. The change in moderate drought, defined by the 20th percentile of the relevant 1×CO2 distribution, was calculated. SPI, based solely on precipitation, shows little change in the proportion of the land surface in drought. All the other indices, which include a measure of the atmospheric demand for moisture, show a significant increase with an additional 5%–45% of the land surface in drought. There are large uncertainties in regional changes in drought. Regions where the precipitation decreases show a reproducible increase in drought across ensemble members and indices. In other regions the sign and magnitude of the change in drought is dependent on index definition and ensemble member, suggesting that the selection of appropriate drought indices is important for impact studies. [ABSTRACT FROM AUTHOR]

Numerous modeling studies have shown that soil moisture anomalies in later spring have a significant effect on the summer rainfall anomalies in North America. On the other hand, the role of soil moisture in forming monsoonal precipitation in East Asia has not been identified. This study attempts to clarify the importance of soil moisture on the summer rainfall in late spring in East Asia. The National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) is utilized for 3-month (June–August) simulations in 1998 (above-normal precipitation year) and 1997 (below-normal precipitation year). Initial and boundary conditions are derived from the NCEP–Department of Energy (DOE) reanalysis. The control run uses the initial soil moisture from the reanalysis, whereas it is set as a saturation and wilting point for “wet” and “dry” experiments, respectively. The impact of soil moisture anomalies on the simulated summer rainfall in East Asia is not significant. The change in precipitation between the wet and dry experiments is about 10%. A conflict between the local feedback of soil moisture and a change in large-scale circulations associated with the summertime monsoonal circulation in East Asia can be attributed as a reason for this anomaly. It is found that enhanced (suppressed) evaporation from the soil to the atmosphere in wet (dry) initial soil moisture reduces (increases) the land–sea contrast between East Asia and the Pacific Ocean, leading to a weakened sensitivity of the monsoonal circulations to the initial soil moisture. It can be concluded that the impact of the initial soil moisture is significant on the dynamic circulation in East Asia. [ABSTRACT FROM AUTHOR]

In the presence of uncertain initial conditions and soil hydraulic properties, land surface model (LSM) performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the near-surface soil moisture (θg), as observed from a remote platform. In this paper the possibility of merging observations and the model optimally for providing robust predictions of root-zone soil moisture (θ2) is demonstrated. An assimilation approach that assimilates θg through the ensemble Kalman filter (EnKF) and provides a physics-based update of θ2 is developed. This approach, as with other common soil moisture assimilation approaches, may fail when a key LSM parameter, for example, the saturated hydraulic conductivity (ks), is estimated poorly. This leads to biased model errors producing a violation of a main assumption (model errors with zero mean) of the EnKF. For overcoming this model bias an innovative assimilation approach is developed that accepts this violation in the early model run times and dynamically calibrates all the components of the ks ensemble as a function of the persistent bias in root-zone soil moisture, allowing one to remove the model bias, restore the fidelity to the EnKF requirements, and reduce the model uncertainty. The robustness of the proposed approach is also examined in sensitivity analyses. [ABSTRACT FROM AUTHOR]

A 10 000-yr unforced simulation with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mark 2 coupled global climatic model has been used to investigate the occurrence of heat waves over the globe. Results are presented for both seasonal (summer mean) and daily heat waves. Geographical distributions of the occurrence rates of these heat waves are shown for various magnitudes of surface temperature anomalies. The heat waves have specific geographical preferences with regions where relatively frequent, intense, and long-lasting heat waves occur. Time series over all 10 000 yr of the heat waves for the selected model grid boxes illustrate the differing temporal variabilities at these locations, as well as identifying the occurrences of extreme heat waves. To this end, the observed European heat wave of 2003 was simulated remarkably well in its overall characteristics; it occurs once in this simulation. Heat waves for various continental locations are shown to occur as isolated spatial and temporal events, and not as part of larger-scale systems over continental-size domains, suggesting stochastic forcing as a contributor to the initiation of the heat waves. Regional plots of selected heat waves at monthly intervals illustrated the considerable spatial variability, progression, and variation in the intensity of the heat waves. Comparison of year-long daily surface temperature anomalies for heat-wave years for simulated and observed conditions at individual model grid boxes indicated substantial agreement, while spatial plots permitted the progress of a short-term heat wave over the United States to be followed. Multidecadal time series plots of intense heat waves also showed basic similarities between the simulation and observations, despite the brevity of the latter. The simulated time series suggest that more extreme heat waves than currently are observed, owing to the brevity of the observations, may be a possibility as a consequence solely of natural variability. An examination of the physical processes associated with a heat wave showed mutually consistent climatic relationships, such that a heat wave was associated with reduced rainfall and consequently reduced soil moisture content, evaporation, and cloud cover, and increased insolation at the surface. These combined changes created the surface temperature increase intrinsic to the heat wave. All heat waves examined for different regions experienced negative rainfall anomalies prior to a heat wave. The cause of these rainfall anomalies was not readily apparent. While an ENSO influence on heat waves is shown to exist in the simulation, not all ENSO events produce heat waves, suggesting that stochastic influences may determine when a major heat wave occurs in conjunction with these events. The limitations of the adequacy of the model ENSO may, however, have had an influence in this regard. [ABSTRACT FROM AUTHOR]

Outputs from two ensembles of atmospheric model simulations for 1951–98 define the influence of “realistic” land surface wetness on seasonal precipitation predictability in boreal summer. The ensembles consist of one forced with observed sea surface temperatures (SSTs) and the other forced with realistic land surface wetness as well as SSTs. Predictability was determined from correlations between the time series of simulated and observed precipitation. The ratio of forced variance to total variance determined potential predictability. Predictability occurred over some land areas adjacent to tropical oceans without land wetness forcing. On the other hand, because of the chaotic nature of the atmosphere, considerable parts of the land areas of the globe did not even show potential predictability with both land wetness and SST forcings. The use of land wetness forcing enhanced predictability over semiarid regions. Such semiarid regions are generally characterized by a negative correlation between fluxes of latent heat and sensible heat from the land surface, and are “water-regulating” areas where soil moisture plays a governing role in land–atmosphere interactions. Actual seasonal prediction may be possible in these regions if slowly varying surface conditions can be estimated in advance. In contrast, some land regions (e.g., south of the Sahel, the Amazon, and Indochina) showed little predictability despite high potential predictability. These regions are mostly characterized by a positive correlation between the surface fluxes, and are “radiation-regulating” areas where the atmosphere plays a leading role. Improvements in predictability for these regions may require further improvements in model physics. [ABSTRACT FROM AUTHOR]

A statistical model based on canonical correlation analysis (CCA) was used to explore climatic associations and predictability of June–August (JJA) maximum and minimum surface air temperatures (Tmax and Tmin) as well as the frequency of Tmax daily extremes (Tmax90) in the central and western United States (west of 90°W). Explanatory variables are monthly and seasonal Pacific Ocean SST (PSST) and the Climate Division Palmer Drought Severity Index (PDSI) during 1950–2001. Although there is a positive correlation between Tmax and Tmin, the two variables exhibit somewhat different patterns and dynamics. Both exhibit their lowest levels of variability in summer, but that of Tmax is greater than Tmin. The predictability of Tmax is mainly associated with local effects related to previous soil moisture conditions at short range (one month to one season), with PSST providing a secondary influence. Predictability of Tmin is more strongly influenced by large-scale (PSST) patterns, with PDSI acting as a short-range predictive influence. For both predictand variables (Tmax and Tmin), the PDSI influence falls off markedly at time leads beyond a few months, but a PSST influence remains for at least two seasons. The maximum predictive skill for JJA Tmin, Tmax, and Tmax90 is from May PSST and PDSI. Importantly, skills evaluated for various seasons and time leads undergo a seasonal cycle that has maximum levels in summer. At the seasonal time frame, summer Tmax prediction skills are greatest in the Midwest, northern and central California, Arizona, and Utah. Similar results were found for Tmax90. In contrast, Tmin skill is spread over most of the western region, except for clusters of low skill in the northern Midwest and southern Montana, Idaho, and northern Arizona. [ABSTRACT FROM AUTHOR]

Observed vegetation feedbacks on temperature and precipitation are assessed across the United States using satellite-based fraction of photosynthetically active radiation (FPAR) and monthly climate data for the period of 1982–2000. This study represents the first attempt to spatially quantify the observed local impact of vegetation on temperature and precipitation over the United States for all months and by season. Lead–lag correlations and feedback parameters are computed to determine the regions where vegetation substantially impacts the atmosphere and to quantify this forcing. Temperature imposes a significant instantaneous forcing on FPAR, while precipitation's impact on FPAR is greatest at one-month lead, particularly across the prairie. An increase in vegetation raises the surface air temperature by absorbing additional radiation and, in some cases, masking the high albedo of snow cover. Vegetation generally exhibits a positive forcing on temperature, strongest in spring and particularly across the northern states. The local impact of FPAR on precipitation appears to be spatially inhomogeneous and relatively weak, potentially due to the atmospheric transport of transpired water. The computed feedback parameters can be used to evaluate vegetation–climate interactions simulated by models with dynamic vegetation. [ABSTRACT FROM AUTHOR]

The ensemble rainfall forecasts over the Sahel for July–September (JAS) from the NCEP Coupled Forecast System (CFS) were evaluated for the period 1981–2002. The comparison with the gauge-based precipitation analysis indicates that the predicted Sahel rainfall is light and exhibits little interannual variability. The rain belt is shifted about 4° southward. One major source of rainfall errors comes from the erroneous sea surface temperature (SST) forecasts. The systematic SST error pattern has positive errors in the North Pacific and the North Atlantic and negative errors in the tropical Pacific and the southern oceans. It resembles the decadal SST mode, which has a significant influence on rainfall over the Sahel. Because the systematic SST errors were not corrected during the forecasts, persistent errors serve as an additional forcing to the atmosphere. The second source of error is from the soil moisture feedback, which contributes to the southward shift of rainfall and dryness over West Africa. This was demonstrated by the comparison between simulations (SIMs) and the Atmospheric Model Intercomparison Project (AMIP) run. Both are forced with observed SSTs. The SIMs initialized at the end of June have realistic soil moisture and do not show the southward shift of rainfall. The AMIP, which predicts soil moisture, maintains the dryness through the summer over the Sahel. For AMIP, the decreased rainfall is contributed by the decreased evaporation (E) due to the dry soil and the shift of the large temperature gradients southward. In response, the African easterly jet (AEJ) shifts southward. Since this jet is the primary source of energy for the African waves and their associated mesoscale convective systems, these too shift southward. This negative feedback contributes to increased dryness over the Sahel. [ABSTRACT FROM AUTHOR]

A workshop on enhancing climate change indices in South America was held in Maceió, Brazil, in August 2004. Scientists from eight southern countries brought daily climatological data from their region for a meticulous assessment of data quality and homogeneity, and for the preparation of climate change indices that can be used for analyses of changes in climate extremes. This study presents an examination of the trends over 1960–2000 in the indices of daily temperature extremes. The results indicate no consistent changes in the indices based on daily maximum temperature while significant trends were found in the indices based on daily minimum temperature. Significant increasing trends in the percentage of warm nights and decreasing trends in the percentage of cold nights were observed at many stations. It seems that this warming is mostly due to more warm nights and fewer cold nights during the summer (December–February) and fall (March–May). The stations with significant trends appear to be located closer to the west and east coasts of South America. [ABSTRACT FROM AUTHOR]

Previous studies show that the climatological precipitation over South America, particularly the Nordeste region, is influenced by the presence of the African continent. Here the influence of African topography and surface wetness on the Atlantic marine ITCZ (AMI) and South American precipitation are investigated. Cross-equatorial flow over the Atlantic Ocean introduced by north–south asymmetry in surface conditions over Africa shifts the AMI in the direction of the flow. African topography, for example, introduces an anomalous high over the southern Atlantic Ocean and a low to the north. This results in a northward migration of the AMI and dry conditions over the Nordeste region. The implications of this process on variability are then studied by analyzing the response of the AMI to soil moisture anomalies over tropical Africa. Northerly flow induced by equatorially asymmetric perturbations in soil moisture over northern tropical Africa shifts the AMI southward, increasing the climatological precipitation over northeastern South America. Flow associated with an equatorially symmetric perturbation in soil moisture, however, has a very weak cross-equatorial component and very weak influence on the AMI and South American precipitation. The sensitivity of the AMI to soil moisture perturbations over certain regions of Africa can possibly improve the skill of prediction. [ABSTRACT FROM AUTHOR]

The role of the land surface in contributing to the potential predictability of the boreal summer climate is investigated with a coupled land–atmosphere climate model. Ensemble simulations for 1982–99 have been conducted with specified observed sea surface temperatures (SSTs). Several treatments of the land surface are investigated: climatological land surface initialization, realistic initialization of soil wetness, and a series of experiments where downward surface fluxes over land are replaced with observed proxies of precipitation, shortwave, and longwave radiation. Without flux replacement the model exhibits strong drift in soil wetness and both systematic errors and poor simulation of interannual variations of precipitation and near-surface temperature. With flux replacement there are large improvements in simulation of both spatial patterns and interannual variability of precipitation and surface temperature. The land surface apparently does contribute, through positive feedback with the atmosphere, to regional climate anomalies. However, because of the sizeable noise component in precipitation, the strong land–atmosphere feedback may not translate into reliable enhancements in predictability, particularly in years of weak anomalies in the land surface initial conditions at the start of boreal summer. [ABSTRACT FROM AUTHOR]

Using 19 yr of Chinese soil moisture data from 1981 to 1999, the authors evaluate soil moisture in three reanalysis outputs: the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis 1 (R-1); and the NCEP–Department of Energy (DOE) reanalysis 2 (R-2) over China. R-2 shows improved interannual variability and better seasonal patterns of soil moisture than R-1 as the result of the incorporation of observed precipitation, but not for all stations. ERA-40 produces a better mean value of soil moisture for most Chinese stations and good interannual variability. Limited observations in the spring indicate a spring soil moisture peak for most of the stations. ERA-40 generally reproduced this event, while R-1 or R-2 generally did not capture this feature, either because the soil was already saturated or the deep soil layer was too thick and damped such a response. ERA-40 and R-1 have a temporal time scale comparable to observations, but R-2 has a memory of nearly 5 months for the growing season, about twice the temporal scale of the observations. The cold season tends to prolong soil moisture memory by about 3 months for R-2 and 1 month for ERA-40. The unrealistic long temporal scale of R-2 can be attributed to the deep layer of the land surface model, which is too thick and dominates the soil moisture variability. R-1 has the same land surface scheme as R-2, but shows a temporal scale close to observations, which is actually because of soil moisture nudging to a fixed climatology. This new long time series of observed soil moisture will prove valuable for other studies of climate change, remote sensing, and model evaluation. [ABSTRACT FROM AUTHOR]

Two simple soil moisture models useful for drought monitoring and climate change studies were proposed, based on 4-yr ground observations of root-zone soil moisture in Sahelian Niger. One is a water balance model that calculates soil moisture using daily precipitation and temperature; the other is a statistical model, a modified antecedent precipitation index calculated using only daily precipitation. Both models estimated seasonal and interannual variations in the observed soil moisture quantitatively well; the estimations correlated strongly (0.94) with the observations. [ABSTRACT FROM AUTHOR]

Interannual variations of Australian climate are strongly linked to the El Niño-Southern Oscillation (ENSO) phenomenon. However, the impact of other mechanisms on prediction, such as atmosphere-land surface interactions, has been less frequently investigated. Here, the impact of soil moisture variability on interannual climate variability and predictability is examined using the Bureau of Meteorology Research Centre atmospheric general circulation model. Two sets of experiments are run, each with five different initial conditions. In the first set of experiments, soil moisture is free to vary in response to atmospheric forcing in each experiment according to a set of simple prognostic equations. A potential predictability index is computed as the ratio of the model's internal variability to its external forced variability. This estimates the level of predictability obtained assuming perfect knowledge of future ocean surface temperatures. A second set of five experiments with prescribed soil moisture is performed. A comparison between these two sets of experiments reveals that fluctuations of soil moisture increase the persistence, the variance, and the potential predictability of surface temperature and rainfall. The interrelationship between these two variables is also strongly dependent upon the soil water content. Results are particularly marked over Australia in this model. A novel feature of this study is the focus on the effectiveness of ENSO-based statistical seasonal forecasting over Australia. Forecasting skill is shown to be crucially dependent upon soil moisture variability over the continent. In fact, surface temperature forecasts in this manner are not possible without soil moisture variability. This result suggests that a better representation of land-surface interaction has the potential to increase the skill of seasonal prediction schemes. [ABSTRACT FROM AUTHOR]

Month-long integrations with a regional climate model covering Europe and the Northern Atlantic are utilized to study the sensitivity of the summertime European precipitation climate with respect to the continental-scale soil moisture content. Experiments are conducted for July 1990 and 1993. For each of the two months, the control experiment with the initial soil water distribution derived from the operational ECMWF analysis is compared against two sensitivity experiments with dry and wet initial soil moisture distributions. The results demonstrate that summertime European precipitation climate in a belt ;1000 km wide between the wet Atlantic and the dry Mediterranean climate heavily depends upon the soil moisture content. In this belt, changes in monthly mean precipitation amount to about half of the changes in mean evapotranspiration. Budget analysis of water substance over selected subdomains demonstrate that the simulated sensitivity cannot be interpreted with the classical recycling mechanism, that is, the surplus of precipitation that falls over wet (as compared to dry) soils does not directly derive from evapotranspiration. Rather, the surplus of precipitation primarily originates from water vapor extracted from the ambient atmospheric flow. Thus, the soil--precipitation feedback must rely on some indirect mechanism, whereby wet soils increase the efficiency of convective precipitation processes. In order to isolate the physical mechanisms underlying the soil--precipitation feedback, a detailed analysis including an investigation of the mean diurnal cycle throughout the integration period is performed. The key elements of the feedback are the following. First, wet soils (small Bowen ratios) imply the buildup of a comparatively shallow boundary layer. The surface fluxes of heat and moisture are thus concentrated into a comparatively small volume of air, leading to the buildup of high values of low-level moist entropy, thereby providing a... [ABSTRACT FROM AUTHOR]