Abstract

Soil moisture–atmosphere coupling is a key process underlying climate variability and change over land. The control of soil moisture (SM) on evapotranspiration (ET) is a necessary condition for soil moisture to feed back onto surface climate. Here we investigate how this control manifests itself across simulations from the CMIP5 ensemble, using correlation analysis focusing on the interannual (summertime) time scale. Analysis of CMIP5 historical simulations indicates significant model diversity in SM–ET coupling in terms of patterns and magnitude. We investigate the relationship of this spread with differences in background simulated climate. Mean precipitation is found to be an important driver of model spread in SM–ET coupling but does not explain all of the differences, presumably because of model differences in the treatment of land hydrology. Compared to observations, some land regions appear consistently biased dry and thus likely overly soil moisture–limited. Because of ET feedbacks on air temperature, differences in SM–ET coupling induce model uncertainties across the CMIP5 ensemble in mean surface temperature and variability. We explore the relationships between model uncertainties in SM–ET coupling and climate projections. In particular over mid-to-high-latitude continental regions of the Northern Hemisphere but also in parts of the tropics, models that are more soil moisture–limited in the present tend to warm more in future projections, because they project less increase in ET and (in midlatitudes) greater increase in incoming solar radiation. Soil moisture–atmosphere processes thus contribute to the relationship observed across models between summertime present-day simulated climate and future warming projections over land.

Abstract

Evapotranspiration (ET) is a key process affecting terrestrial hydroclimate, as it modulates the land surface carbon, energy, and water budgets. Evapotranspiration mainly consists of the sum of three components: plant transpiration, soil evaporation, and canopy interception. Here we investigate how the partitioning of ET into these three main components is represented in CMIP5 model simulations of present and future climate. A large spread exists between models in the simulated mean present-day partitioning; even the ranking of the different components in the global mean differs between models. Differences in the simulation of the vegetation leaf area index appear to be an important cause of this spread. Although ET partitioning is not accurately known globally, existing global estimates suggest that CMIP5 models generally underestimate the relative contribution of transpiration. Differences in ET partitioning lead to differences in climate characteristics over land, such as land–atmosphere fluxes and near-surface air temperature. On the other hand, CMIP5 models simulate robust patterns of future changes in ET partitioning under global warming, notably a marked contrast between decreased transpiration and increased soil evaporation in the tropics, whereas transpiration and evaporation both increase at higher latitudes and both decrease in the dry subtropics. Idealized CMIP5 simulations from a subset of models show that the decrease in transpiration in the tropics largely reflects the stomatal closure effect of increased atmospheric CO₂ on plants (despite increased vegetation from CO₂ fertilization), whereas changes at higher latitudes are dominated by radiative CO₂ effects, with warming and increased precipitation leading to vegetation increase and simultaneous (absolute) increases in all three ET components.

Abstract

By using a detailed agricultural and climate dataset over Burkina-Faso and simple assumptions regarding the form of an insurance contract, the authors investigate the potential economic efficiency for farmers of a weather-index insurance system in this country. To do so, the results of more than 3000 simulated contracts applied to 30 districts, 21 yr (1984–2004), and five crops (cotton, millet, sorghum, maize, and groundnut) are explored. It is found that such an insurance system, even based on a simple weather index like cumulative rainfall during the rainy season, can present a significant economic efficiency for some crops and districts. The determinants of the efficiency of such contracts are analyzed in terms of yield/index correlations and yield variability. As a consequence of these two main determinants, the farmer’s gain from an insurance contract is higher in the driest part of the country. In the same way, maize and groundnuts are the most suitable to implement an insurance system since their respective yields show a large variance and a generally high correlation with the weather index. However, the implementation of a real weather-index insurance system in West Africa raises a number of key practical issues related to cultural, economic, and institutional aspects.

Abstract

Prior studies have highlighted West Africa as a regional hotspot of land–atmosphere coupling. This study focuses on the large-scale influence of soil moisture variability on the mean circulation and precipitation in the West African monsoon. A suite of six models from the Global Land–Atmosphere Coupling Experiment (GLACE)-CMIP5 is analyzed. In this experiment, model integrations were performed with soil moisture prescribed to a specified climatological seasonal cycle throughout the simulation, which severs the two-way coupling between soil moisture and the atmosphere. Comparison with the control (interactive soil moisture) simulations indicates that mean June–September monsoon precipitation is enhanced when soil moisture is prescribed. However, contrasting behavior is evident over the seasonal cycle of the monsoon, with core monsoon precipitation enhanced with prescribed soil moisture but early-season precipitation reduced, at least in some models. These impacts stem from the enhancement of evapotranspiration at the dry poleward edge of the monsoon throughout the monsoon season, when soil moisture interactivity is suppressed. The early-season decrease in rainfall with prescribed soil moisture is associated with a delayed poleward advancement of the monsoon, which reflects the relative cooling of the continent from enhanced evapotranspiration, and thus a reduced land–ocean thermal contrast, prior to monsoon onset. On the other hand, during the core/late monsoon season, surface evaporative cooling modifies meridional temperature gradients and, through these gradients, alters the large-scale circulation: the midlevel African easterly jet is displaced poleward while the low-level westerlies are enhanced; this enhances precipitation. These results highlight the remote impacts of soil moisture variability on atmospheric circulation and precipitation in West Africa.

Abstract

A new methodology for assessing the impact of surface heat fluxes on precipitation is applied to data from the North American Regional Reanalysis (NARR) and to output from the Geophysical Fluid Dynamics Laboratory’s Atmospheric Model 2.1 (AM2.1). The method assesses the sensitivity of afternoon convective rainfall frequency and intensity to the late-morning partitioning of latent and sensible heating, quantified in terms of evaporative fraction (EF). Over North America, both NARR and AM2.1 indicate sensitivity of convective rainfall triggering to EF but no appreciable influence of EF on convective rainfall amounts. Functional relationships between the triggering feedback strength (TFS) metric and mean EF demonstrate the occurrence of stronger coupling for mean EF in the range of 0.6 to 0.8. To leading order, AM2.1 exhibits spatial distributions and seasonality of the EF impact on triggering resembling those seen in NARR: rainfall probability increases with higher EF over the eastern United States and Mexico and peaks in Northern Hemisphere summer. Over those regions, the impact of EF variability on afternoon rainfall triggering in summer can explain up to 50% of seasonal rainfall variability. However, the AM2.1 metrics also exhibit some features not present in NARR, for example, strong coupling extending northwestward from the central Great Plains into Canada. Sources of disagreement may include model hydroclimatic biases that affect the mean patterns and variability of surface flux partitioning, with EF variability typically much lower in NARR. Finally, the authors also discuss the consistency of their results with other assessments of land–precipitation coupling obtained from different methodologies.

Abstract

A recent theory proposes that inland continental regions are in a state of surface flux equilibrium (SFE), in which tight coupling between the land and atmosphere allow estimation of the Bowen ratio at daily to monthly time scales solely from atmospheric measurements, without calibration, even when the land surface strongly constrains the surface energy budget. However, since the theory has only been evaluated at quasi-point spatial scales using eddy covariance measurements with limited global coverage, it is unclear if it is applicable to the larger spatial scales relevant to studies of global climate. In this study, SFE estimates of the Bowen ratio are combined with satellite observations of surface net radiation to obtain large-scale estimates of latent heat flux λE. When evaluated against multiyear mean annual λE obtained from catchment water balance estimates from 221 catchments across the United States, the resulting error statistics are comparable to those in the catchment water balance estimates themselves. The theory is then used to diagnostically estimate λE using historical simulations from 26 CMIP6 models. The resulting SFE estimates are typically at least as accurate as the CMIP6 model’s simulated λE, when compared with catchment water balance estimates. Globally, there is broad spatial and temporal agreement between CMIP6 model SFE estimates and the CMIP6 model’s simulated λE, although SFE likely overestimates λE in some arid regions. We conclude that SFE applies reasonably at large spatial scales relevant to climate studies, and is broadly reproduced in climate models.

Abstract

Understanding how different physical processes can shape the probability distribution function (PDF) of surface temperature, in particular the tails of the distribution, is essential for the attribution and projection of future extreme temperature events. In this study, the contribution of soil moisture–atmosphere interactions to surface temperature PDFs is investigated. Soil moisture represents a key variable in the coupling of the land and atmosphere, since it controls the partitioning of available energy between sensible and latent heat flux at the surface. Consequently, soil moisture variability driven by the atmosphere may feed back onto the near-surface climate—in particular, temperature. In this study, two simulations of the current-generation Geophysical Fluid Dynamics Laboratory (GFDL) Earth System Model, with and without interactive soil moisture, are analyzed in order to assess how soil moisture dynamics impact the simulated climate. Comparison of these simulations shows that soil moisture dynamics enhance both temperature mean and variance over regional “hotspots” of land–atmosphere coupling. Moreover, higher-order distribution moments, such as skewness and kurtosis, are also significantly impacted, suggesting an asymmetric impact on the positive and negative extremes of the temperature PDF. Such changes are interpreted in the context of altered distributions of the surface turbulent and radiative fluxes. That the moments of the temperature distribution may respond differentially to soil moisture dynamics underscores the importance of analyzing moments beyond the mean and variance to characterize fully the interplay of soil moisture and near-surface temperature. In addition, it is shown that soil moisture dynamics impacts daily temperature variability at different time scales over different regions in the model.

Abstract

We investigate the role of moisture transport and recycling in characterizing two recent drought events in Texas (2011) and the Upper Midwest (2012) by analyzing the precipitation, evapotranspiration, precipitable water, and soil moisture data from the Climate Forecast System version 2 (CFSv2) analysis. Next, we evaluate the CFSv2 forecasts in terms of their ability to capture different drought signals as reflected in the analysis data. Precipitation from both sources is partitioned into recycled and advected components using a moisture accounting–based precipitation recycling model. All four variables reflected drought signals through their anomalously low values, while precipitation and evapotranspiration had the strongest signals. Drought in Texas was dominated by the differences in moisture transport, whereas in the Upper Midwest, the absence of strong precipitation-generating mechanisms was a crucial factor. Reduced advection from the tropical and midlatitude Atlantic contributed to the drought in Texas. The Upper Midwest experienced reduced contributions from recycling, terrestrial sources, the midlatitude Pacific, and the tropical Atlantic. In both cases, long-range moisture transport from oceanic sources was reduced during the corresponding drought years. June and August in Texas and July and August in the Upper Midwest were the driest months, and in both cases, drought was alleviated by the end of August. Moisture from terrestrial sources most likely contributed to alleviating drought intensity in such conditions, even with negative anomalies. The forecasts showed noticeable differences as compared to the analysis for multiple variables in both regions, which could be attributed to several factors as discussed in this paper.

Abstract

Understanding vulnerabilities of continental precipitation to changing climatic conditions is of critical importance to society at large. Terrestrial precipitation is fed by moisture originating as evaporation from oceans and from recycling of water evaporated from continental sources. In this study, continental precipitation and evaporation recycling processes in the Earth system model GFDL-ESM2G are shown to be consistent with estimates from two different reanalysis products. The GFDL-ESM2G simulations of historical and future climate also show that values of continental moisture recycling ratios were systematically higher in the past and will be lower in the future. Global mean recycling ratios decrease 2%–3% with each degree of temperature increase, indicating the increased importance of oceanic evaporation for continental precipitation. Theoretical arguments for recycling changes stem from increasing atmospheric temperatures and evaporative demand that drive increases in evaporation over oceans that are more rapid than those over land as a result of terrestrial soil moisture limitations. Simulated recycling changes are demonstrated to be consistent with these theoretical arguments. A simple prototype describing this theory effectively captures the zonal mean behavior of GFDL-ESM2G. Implications of such behavior are particularly serious in rain-fed agricultural regions where crop yields will become increasingly soil moisture limited.

Abstract

Metrics are proposed—that is, a few summary statistics that condense large amounts of data from observations or model simulations—encapsulating the diurnal cycle of precipitation. Vector area averaging of Fourier amplitude and phase produces useful information in a reasonably small number of harmonic dial plots, a procedure familiar from atmospheric tide research. The metrics cover most of the globe but down-weight high-latitude wintertime ocean areas where baroclinic waves are most prominent. This enables intercomparison of a large number of climate models with observations and with each other. The diurnal cycle of precipitation has features not encountered in typical climate model intercomparisons, notably the absence of meaningful “average model” results that can be displayed in a single two-dimensional map. Displaying one map per model guides development of the metrics proposed here by making it clear that land and ocean areas must be averaged separately, but interpreting maps from all models becomes problematic as the size of a multimodel ensemble increases.

Global diurnal metrics provide quick comparisons with observations and among models, using the most recent version of the Coupled Model Intercomparison Project (CMIP). This includes, for the first time in CMIP, spatial resolutions comparable to global satellite observations. Consistent with earlier studies of resolution versus parameterization of the diurnal cycle, the longstanding tendency of models to produce rainfall too early in the day persists in the high-resolution simulations, as expected if the error is due to subgrid-scale physics.