Search

Normalized mutual information (NMI) is a nonparametric measure of the dependence between two variables without assumptions about the shape of their bivariate data distributions, but the implementation and interpretation of NMI in the coupled climate system is more complicated than for linear correlations. This study presents a joint approach combining correlation and NMI to examine land and ocean surface forcing of U.S. drought at varying lead times. Based on the distribution of correlation versus NMI between a source variable (local or remote forcing) and target variable [e.g., summer precipitation in the southern Great Plains (SGP)], newly proposed one-tail significance levels for NMI combined with two-tailed significance levels of correlation enable us to discern linearity and nonlinearity dominant regimes in a more intuitive way. Our analysis finds that NMI can detect strong linear relationships like correlations, but it is not exclusively tuned to linear relationships as correlations are. Also, NMI can further identify nonlinear relationships, particularly when there are clusters and blank areas (high density and low density) in joint probability distributions between source and target variables (e.g., detected between soil moisture conditions in eastern Montana from mid-February to mid-August and summer precipitation in the SGP). The linear and nonlinear information are found to be sometimes mixed and rather convoluted with time, for instance, in the subtropical Pacific of the Southern Hemisphere, revealing relationships that cannot be fully detected by either NMI or correlation alone. Therefore, this joint approach is a potentially powerful tool to reveal complex and heretofore undetected relationships.

Land states can affect the atmosphere through their control of surface turbulent fluxes and the subsequent impact of those fluxes on boundary layer properties. Information theoretic (IT) metrics are ideal to study the strength and type of coupling between surface soil moisture (SM) and land surface heat fluxes (HFs) because they are nonparametric and thus appropriate for the analysis of highly complex Earth systems containing nonlinear cause-and-effect interactions that may have nonnormal distributions. Specifically, a methodology for the estimation of IT metrics from noisy time series is proposed, accounting for random errors in satellite-based SM data. Performance of the proposed method is demonstrated through synthetic tests. Efficacy of the method is greatest for estimates of entropy and mutual information involving SM; improvements to estimates of transfer entropy are significant but less stark. A global depiction of the information flow between SM and HFs is then constructed from observationally based gridded data. This is used as independent verification for two configurations of the ECMWF modeling system: unconstrained open-loop (retrospective forecasts) and constrained by data assimilation (ERA5). Compared to studies that only investigate the linear SM–HF relationships, extended regions of significant terrestrial coupling are found over the globe, as IT metrics enable detection of nonlinear dependencies. The magnitude and spatial variability of coupling strength and type from models show discrepancies with those from observations, highlighting the potential to improve SM and HF covariability within models. Although ERA5 did not perform better than the unconstrained model in very dry climates, its performance is generally superior to that of the unconstrained model across metrics.

Land–atmosphere interactions have been investigated at daily or longer timescales due to limited data availability and large errors for measuring high-frequency variations. Yet coupling at the subdaily timescale is characterized by the diurnal cycle of incoming solar radiation and surface fluxes. Based on flux tower observations, this study investigates the climatology of observed land–atmosphere interactions on subdaily timescales during the warm season. Process-based multivariate metrics are employed to quantitatively measure segmented coupling processes, and mixing diagrams are adopted to demonstrate the integrative moist and thermal energy budget evolution in the atmospheric mixed layer. The land, atmosphere, and combined couplings for the entire daily mean, midday, and midnight periods show different situations to which surface latent and sensible heat fluxes are relevant, and they also reveal the climate sensitivity to soil moisture and surface air temperature. The 24 h coevolution of the moist and thermal energy within the boundary layer traces a particular path on mixing diagrams, exhibiting different degrees of asymmetry (time shifts) in water- and energy-limited locations. Water- and energy-limited processes also show opposing long tails of low humidity during the daytime and nighttime, related to the impact on land and atmospheric couplings of latent heat flux and other diabatic processes like radiative cooling. This study illustrates the necessity of considering the entire diurnal cycle to understand land–atmosphere coupling processes comprehensively in observations and models.

Increased heat-wave frequency across the United States has led to the need for improved predictability of heat-wave events. A detailed understanding of land–atmosphere interactions and the relationship between soil moisture and temperature extremes could provide useful information for prediction. This study identifies, for many locations, a threshold of soil moisture below which there is an increase in the sensitivity of atmospheric temperature to declining soil moisture. This shift to a hypersensitive regime causes the atmosphere to be more susceptible to atmospherically driven heat-wave conditions. The soil moisture breakpoint where the regime shift occurs is estimated using segmented regression applied to observations and reanalysis data. It is shown that as the soil gets drier, there is a concomitant change in the rate of decrease in latent heat flux and increase in sensible heat flux leading to a strong positive feedback of increased air temperature near the surface, which further dries out the soil. Central, southwestern, and southeastern parts of the United States seem to have regions of clear regime shifts, while the eastern part of the United States generally does not get dry enough to reveal significant breakpoints. Sensible heat flux is seen to be a primary driver of this increased temperature sensitivity aided by the drop in latent heat flux. An investigation of flux tower sites verifies the breakpoint–flux relationships found in reanalysis data. Accurate estimation of these breakpoints can contribute to improved heat-wave prediction.

In addition to remote SST forcing, realistic representation of land forcing (i.e., soil moisture) over the United States is critical for a prediction of U.S. severe drought events approximately one season in advance. Using “identical twin” experiments with different land initial conditions (ICs) in the 32-yr (1979–2010) CFSv2 reforecasts (NASA GLDAS-2 reanalysis versus NCEP CFSR), sensitivity and skill of U.S. drought predictions to land ICs are evaluated. Although there is no outstanding performer between the two sets of forecasts with different land ICs, each set shows greater skill in some regions, but their locations vary with forecast lead time and season. The 1999 case study demonstrates that although a pattern of below-normal SSTs in the Pacific in the fall and winter is realistically reproduced in both reforecasts, GLDAS-2 land initial states display a stronger east–west gradient of soil moisture, particularly drier in the eastern United States and more consistent with observations, leading to warmer surface temperature anomalies over the United States. Anomalies lasting for one season are accompanied by more persistent barotropic (warm core) anomalous high pressure over CONUS, which results in better prediction skill of this drought case up to 4 months in advance in the reforecasts with GLDAS-2 land ICs. Therefore, it is essential to minimize the uncertainty of land initial states among the current land analyses for improving U.S. drought prediction on seasonal time scales.

The control of latent heat flux (LE) by soil moisture (SM) is a key process affecting the moisture and energy budgets at the land-atmosphere interface. SM:LE coupling relationships are conventionally examined using metrics involving temporal correlation. However, such a traditional linear approach, which fits a straight line across the full SM:LE space to evaluate the dependency, leaves out certain critical information: nonlinear SM:LE relationships and the long-recognized thresholds that lead to dramatically different behavior in different ranges of soil moisture, delineating a dry regime, a transitional regime of high sensitivity, and a wet (energy-limited) regime. Using data from climate models, reanalyses, and observationally-constrained datasets, global patterns of SM:LE regimes are determined by segmented regression. Mutual information analysis is applied only for days when SM is in the transitional regime between critical points defining high sensitivity of LE to SM variations. Sensitivity is further decomposed into linear and nonlinear components. Results show discrepancies in the global patterns of existing SM regimes, but general consistencies among the linear and nonlinear components of SM:LE coupling. This implies that although models simulate differing surface hydroclimates, once SM is in the transitional regime, the locations where LE closely interacts with SM are well-captured and resemble the conventional distribution of “hot spots” of land-atmosphere interactions. This indicates that only the transitional SM regime determines the strength of coupling, and attention should focused on when this regime occurs. This framework can also be applied to investigate extremes and the shifting surface hydroclimatology in a warming climate.

The NCEP CFSv2 ensemble reforecasts initialized with different land surface analyses for the period of 1979–2010 have been conducted to assess the effect of uncertainty in land initial states on surface air temperature prediction. The two observation-based land initial states are adapted from the NCEP CFS Reanalysis (CFSR) and the NASA GLDAS-2 analysis; atmosphere, ocean, and ice initial states are identical for both reforecasts. This identical-twin experiment confirms that the prediction skill of surface air temperature is sensitive to the uncertainty of land initial states, especially in soil moisture and snow cover. There is no distinct characteristic that determines which set of the reforecasts performs better. Rather, the better performer varies with the lead week and location for each season. Estimates of soil moisture between the two land initial states are significantly different with an apparent north–south contrast for almost all seasons, causing predicted surface air temperature discrepancies between the two sets of reforecasts, particularly in regions where the magnitude of initial soil moisture difference lies in the top quintile. In boreal spring, inconsistency of snow cover between the two land initial states also plays a critical role in enhancing the discrepancy of predicted surface air temperature from week 5 to week 8. Our results suggest that a reduction of the uncertainty in land surface properties among the current land surface analyses will be beneficial to improving the prediction skill of surface air temperature on subseasonal time scales. Implications of a multiple land surface analysis ensemble are also discussed.

Seamless prediction means bridging discrete short-term weather forecasts valid at a specific time and time-averaged forecasts at longer periods. Subseasonal predictions span this time range and must contend with this transition. Seamless forecasts and seamless validation methods go hand-in-hand. Time-averaged forecasts often feature a verification window that widens in time with growing forecast leads. Ideally, a smooth transition across daily to monthly time scales would provide true seamlessness—a generalized approach is presented here to accomplish this. We discuss prior attempts to achieve this transition with individual weighting functions before presenting the two-parameter Hill equation as a general weighting function to blend discrete and time-averaged forecasts, achieving seamlessness. The Hill equation can be tuned to specify the lead time at which the discrete forecast loses dominance to time-averaged forecasts, as well as the swiftness of the transition with lead time. For this application, discrete forecasts are defined at any lead time using a Kronecker delta weighting, and any time-averaged weighting approach can be used at longer leads. Time-averaged weighting functions whose averaging window widens with lead time are used. Example applications are shown for deterministic and ensemble forecasts and validation and a variety of validation metrics, along with sensitivities to parameter choices and a discussion of caveats. This technique aims to counterbalance the natural increase in uncertainty with forecast lead. It is not meant to construct forecasts with the highest skill, but to construct forecasts with the highest utility across time scales from weather to subseasonal in a single seamless product.

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

Models have historically been the source of global soil moisture (SM) analyses and estimates of land-atmosphere coupling, even though they are usually calibrated and validated only locally. Satellite-based analyses have grown in fidelity and duration, offering an independent observationally-based alternative. However, satellite-retrieved SM time series include random and periodic errors that degrade estimates of land-atmosphere coupling, including correlations with other variables. This study proposes a mathematical approach to adjust daily time series of the European Space Agency (ESA) Climate Change Initiative (CCI) satellite SM product using information from physical-based land surface model (LSM) datasets using a Fourier transform time-filtering method to match the temporal power spectra locally to the LSMs, which tend to agree well with in situ observations. When the original and time-filtered SM products are evaluated against ground-based SM measurements over the conterminous U.S., Europe, and Australia, results show the filtered SM has significantly improved subseasonal variability. The skill of the time-filtered SM is increased in temporal correlation by ∼0.05 over all analysis domains without introducing spurious regional patterns, affirming the stochastic nature of noise in satellite estimates, and skill improvement is found for nearly all land cover classes, especially savannas and grassland. Autocorrelation-based soil moisture memory (SMM), and the derived random component of soil moisture error (SME) are used to investigate the improvement of SM features. Time filtering reduces the random noise from the satellite-based SM product that is not explainable by physically-based SM dynamics; SME is usually diminished and the increased SMM is generally statistically significant.

Contemporary Earth system models mostly ignore the sub‐grid scale (SGS) heterogeneous coupling between the land surface and atmosphere. To aid in the development of coupled land and atmosphere SGS parameterizations for global models, we present a study of different aspects of highly realistic sub‐100 km scale land‐surface heterogeneity. The primary experiment is a set of simulations of September 24, 2017 over the Southern Great Plains (SGP) site using the Weather Research and Forecasting (WRF) model with 100‐m horizontal resolution. The overall impact of land‐surface heterogeneity is evaluated by comparing cloud and turbulent kinetic energy (TKE) production in large‐eddy simulations (LESs) using heterogeneous and homogeneous surface fields (namely sensible and latent heat fluxes) specified by an offline field‐scale resolving land‐surface model (LSM). The heterogeneous land surface leads to significantly more cloud and TKE production. We then isolate specific sources of heterogeneity by using selectively domain‐wide averaged fields in the LSM. It is found that heterogeneity in the land surface created by precipitation is effectively responsible for the increases in cloud and TKE production, while rivers and soil type have a negligible impact and land cover has only a small impact. Additional experiments modify the Bowen ratio in the surface fields and the initial wind profile of the heterogeneous case to clarify the results seen. Finally two additional days at the SGP site are simulated showing a similar increase in cloud production in heterogeneous cases.

Recent studies have shown the impacts of historical land-use land-cover changes (i.e., deforestation) on hot temperature extremes; contradictory temperature responses have been found between studies using observations and climate models. However, different characterizations of surface temperature are sometimes used in the assessments: land surface skin temperature Ts is more commonly used in observation-based studies while near-surface air temperature T2m is more often used in model-based studies. The inconsistent use of temperature variables is not inconsequential, and the relationship between deforestation and various temperature changes can be entangled, which complicates comparisons between observations and model simulations. In this study, the responses in the diurnal cycle of summertime Ts and T2m to deforestation are investigated using the Community Earth System Model. For the daily maximum, opposite responses are found in Ts and T2m. Due to decreased surface roughness after deforestation, the heat at the land surface cannot be efficiently dissipated into the air, leading to a warmer surface but cooler air. For the daily minimum, strong warming is found in T2m, which exceeds daytime cooling and leads to overall warming in daily mean temperatures. After comparing several climate models, we find that the models agree in daytime land surface (Ts) warming, but different turbulent transfer characteristics produce discrepancies in T2m. Our work highlights the need to investigate the diurnal cycles of temperature responses carefully in land-cover change studies. Furthermore, consistent consideration of temperature variables should be applied in future comparisons involving observations and climate models.

The interactions between land and atmosphere (with terrestrial and atmospheric coupling segments) play a significant role in weather and climate. A predominant segment of land-atmosphere (L-A) feedbacks is the coupling between soil moisture (SM) and surface heat fluxes, the terrestrial coupling leg. The lack of high-quality long-term globally distributed observations, however, has hindered a robust, realistic identification of the terrestrial leg strength on a global scale. This exploratory study provides insight into how SM signals are translated into surface flux signals through the construction of a global depiction of the terrestrial leg from several recently developed global, gridded, observationally- and satellite-based data sets. The feasibility of producing global gridded estimates of L-A coupling metrics is explored. Five weather and climate models used for subseasonal to seasonal forecasting are confronted with the observational estimates to discern discrepancies that may affect their ability to predict phenomena related to L-A feedbacks, such as drought or heat waves. The terrestrial feedback leg from observations corroborates the “hot spots” of L-A coupling found in modeling studies, but the variances in daily time series of surface fluxes differ markedly. Better agreement and generally higher confidence are seen in metrics using latent heat flux than sensible heat flux. Observational metrics allow for clear stratification of model fidelity that is consistent across seasons, despite observational uncertainty. The results highlight the impact of SM on partitioning available surface energy and illustrate the potential of global observationally-based data sets for the assessment of such relationships in weather and climate models.

Land-surface heterogeneity is known to play an important role in land-surface hydrology, which drives the bottom boundary condition for atmospheric models in numerical weather prediction (NWP) applications. However, the ultimate impact of land-surface heterogeneity on atmospheric boundary layer (ABL) development is still an open problem with implications for sub-grid scale (SGS) parameterizations for both NWP and climate modeling. Large-eddy simulation (LES) is often used to study the effects of land-surface heterogeneity on ABL development, most typically via specified surface fields which are not influenced by the atmosphere (i.e. semi-coupled). Heterogeneous land surfaces have been seen in previous studies to have a significant influence on ABL dynamics, particularly cloud production, in certain cases when semi-coupled to the atmosphere. Here we use the Weather Research and Forecasting (WRF) model as an LES with both semi-coupled and fully-coupled land surfaces to investigate the impact of two-way coupling on the interaction between heterogeneous land surfaces and daytime ABLs. For semi-coupled simulations, the HydroBlocks land-surface model is run offline, driven by 4-km NLDAS-2 meteorology with Stage-IV radar rainfall data, and then used to specify the bottom boundary in WRF. The WRF-Hydro model is used for cases where the land surface is fully coupled to the WRF model. Both land-surface models use the Noah-MP model as their underlying physics package and add both subsurface and overland flow routing. The WRF model uses a 100-m horizontal resolution, and the land-surface models use high resolution (30 m) datasets that were upscaled to match the LES resolution for elevation, landcover, and soil type using NED, NLCD, and POLARIS respectively. These LES experiments are performed over the ARM Southern Great Plains Site atmospheric observatory in Oklahoma during the Summer of 2017 with a grid size of 100 km x 100 km to imitate a single cell in a modern climate model. The impact of land-surface heterogeneity on the atmosphere is evaluated by comparing simulations using the fully heterogeneous land surfaces with simulations where the land surface is homogenized at each timestep, taking a domain-wide spatial mean value at every grid cell. Results are evaluated primarily by the differences in the development of clouds and evolution of turbulent kinetic energy in the ABL.

The predicted increase in drought occurrence and intensity will pose serious threats to global future water and food security. This was hinted by several historically unprecedented droughts over the last two decades, taking place in Europe, Australia, Amazonia or the USA. It has been hypothesised that the strength of these events responded to self-reinforcement processes related to land–atmospheric feedbacks: as rainfall deficits dry out soil and vegetation, the evaporation of land water is reduced, then the local air becomes too dry to yield rainfall, which further enhances drought conditions. Despite the 'local' nature of these feedbacks, their consequences can be remote, as downwind regions may rely on evaporated water transported by winds from drought-affected locations. Following this rationale, droughts may not only self-reinforce locally, due to land atmospheric feedbacks, but self-propagate in the downwind direction, always conditioned on atmospheric circulation. This propagation is not only meteorological but relies on soil moisture drought, and may lead to a downwind cascading of impacts on water resources. However, a global capacity to observe these processes is lacking, and thus our knowledge of how droughts start and evolve, and how this may change as climate changes, remains limited. Furthermore, climate and forecast models are still immature when it comes to representing the influences of land on rainfall.Here, the largest global drought events are studied to unravel the role of land–atmosphere feedbacks during the spatiotemporal propagation of these events. We based our study on satellite and reanalysis records of soil moisture, evaporation, air humidity, winds and precipitation, in combination with a Lagrangian framework that can map water vapor trajectories and explore multi-dimensional feedbacks. We estimate the reduction in precipitation in the direction of drought propagation that is caused by the upwind soil moisture drought, and isolate this effect from the influence of potential evaporation and circulation changes. By doing so, the downwind lack of precipitation caused by upwind soil drought via water vapor deficits, and hence the impact of drought self-propagation, is determined. We show that droughts occurring in dryland regions are particularly prone to self-propagate, as evaporation there tends to respond strongly to enhanced soil stress and precipitation is frequently convective. This kind of knowledge may be used to improve climate and forecast models and can be exploited to develop geo-engineering mitigation strategies to help prevent drought events from aggravating during their early stages.

Past and projected changes in global hydroclimate in Earth system models have been examined. The Budyko framework that relates the partitioning of precipitation into evaporation to a location’s ari...

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis. Fil: Merryfield, William J.. Canadian Centre For Climate Modelling And Analysis; Canadá Fil: Baehr, Johanna. Universitat Hamburg; Alemania Fil: Batté, Lauriane. Centre National de Recherches Météorologiques; Francia Fil: Becker, Emily J.. No especifíca; Fil: Butler, Amy H.. State University of Colorado at Boulder; Estados Unidos Fil: Coelho, Caio A.S.. Instituto Nacional de Pesquisas Espaciais; Brasil Fil: Danabasoglu, Gokhan. National Center for Atmospheric Research; Estados Unidos Fil: Dirmeyer, Paul A.. No especifíca; Fil: Doblasreyes, Francisco. No especifíca; Fil: Domeisen, Daniela I.V.. No especifíca; Fil: Ferranti, Laura. No especifíca; Fil: Ilynia, Tatiana. No especifíca; Fil: Kumar, Arun. No especifíca; Fil: Müller, Wolfgang A.. No especifíca; Fil: Rixen, Michel. World Meteorological Organization; Estados Unidos Fil: Robertson, Andrew W.. International Research Institute For Climate And Society; Estados Unidos Fil: Smith, Doug M.. No especifíca; Fil: Takaya, Yuhei. Japan Meteorological Agency; Japón Fil: Tuma, Matthias. World Meteorological Organization; Estados Unidos Fil: Vitart, Frederic. No especifíca; Fil: White, Christopher J.. University of Strathclyde; Reino Unido Fil: Alvarez, Mariano Sebastián. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Ardilouze, Constantin. No especifíca; Fil: Attard, Hannah. No especifíca; Fil: Baggett, Cory. State University of Colorado - Fort Collins; Estados Unidos Fil: Balmaseda, Magdalena A.. No especifíca; Fil: Beraki, Asmerom F.. University Of Pretoria; Sudáfrica Fil: Bhattacharjee, Partha S.. National Oceanic And Atmospheric Administration; Estados Unidos Fil: Bilbao, Roberto. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; España Fil: De Andrade, Felipe M.. University of Reading; Reino Unido Fil: DeFlorio, Michael J.. No especifíca; Fil: Díaz, Leandro Baltasar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Ehsan, Muhammad Azhar. The Abdus Salam. International Centre for Theoretical Physics; Italia. The Abdus Salam; Italia Fil: Fragkoulidis, Georgios. Johannes Gutenberg Universitat Mainz; Alemania Fil: Grainger, Sam. No especifíca; Fil: Green, Benjamin W.. State University of Colorado at Boulder; Estados Unidos Fil: Hell, Momme C.. No especifíca; Fil: Infanti, Johnna M.. National Oceanic And Atmospheric Administration; Estados Unidos Fil: Isensee, Katharina. No especifíca; Fil: Kataoka, Takahito. No especifíca; Fil: Kirtman, Ben P.. No especifíca; Fil: Klingaman, Nicholas P.. University of Reading; Reino Unido Fil: Lee, June Yi. Pusan National University; Corea del Sur Fil: Mayer, Kirsten. State University of Colorado at Boulder; Estados Unidos Fil: McKay, Roseanna. Monash University; Australia Fil: Mecking, Jennifer V.. University of Southampton; Reino Unido Fil: Miller, Douglas E.. University of Illinois. Urbana - Champaign; Estados Unidos Fil: Neddermann, Nele. No especifíca; Fil: Ng, Ching Ho Justin. University of Princeton; Estados Unidos Fil: Ossó, Albert. University of Graz; Austria Fil: Pankatz, Klaus. Max Planck Institute For Meteorology; Estados Unidos Fil: Peatman, Simon. No especifíca; Fil: Pegion, Kathy. George Mason University; Estados Unidos Fil: Perlwitz, Judith. State University of Colorado at Boulder; Estados Unidos Fil: Recalde Coronel, G. Cristina. Escuela Superior Politecnica del Litoral ; Ecuador Fil: Reintges, Annika. Geomar-Helmholtz Centre for Ocean Research Kiel; Alemania Fil: Renkl, Christoph. Dalhousie University Halifax; Canadá Fil: Solaraju-Murali, Balakrishnan. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; España Fil: Spring, Aaron. No especifíca; Fil: Stan, Cristiana. George Mason University; Estados Unidos Fil: Sun, Y. Qiang. University of Princeton; Estados Unidos Fil: Tozer, Carly R.. No especifíca; Fil: Vigaud, Nicolas. No especifíca; Fil: Woolnough, Steven. University of Reading; Reino Unido Fil: Yeager, Stephen. National Center for Atmospheric Research; Estados Unidos

Land use has been recognized as an important anthropogenic forcing of climate change in recent studies. However, climatic effects of land management practices have been little discussed and compared to land-use impacts. As land-atmosphere interactions via surface fluxes are particularly strong during the warm season, we investigate the impacts of historical land use and present irrigation practices on summer temperatures in the Northern Hemisphere using the most recent version of Community Earth System Model. Our results suggest that historical land use leads to an overall cooling in the middle latitudes and a warming in the tropics, and the sign and magnitude of the changes in temperature depend on the type of land cover change. On the other hand, summer irrigation leads to a significant cooling over many irrigated areas due to enhanced evapotranspiration, and the local cooling is comparable to and even stronger than the land-use effects. Both land use and irrigation can also significantly influence the intensity and frequency of hot extremes. Land use shows stronger impacts during the night through ground heat flux feedback, while irrigation shows stronger impacts during the day through latent heat flux feedback. Our comparison demonstrates the importance of irrigation in local and regional climate, highlighting the necessity of considering such land management practices in future assessments of regional climate change and climate mitigation.