Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.

The Global Land-Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: landatmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel "hot spots" of land-atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model's coupling strength within the broad range of those documented here.

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The 12 weather and climate models participating in the Global Land-Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land-atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength-both the geographic variations within a given model and the model-to-model differences-are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage-an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation-precipitation connection, however, also play a key role.

From 30 September to 2 October 1999 a workshop was held in Gif-sur-Yvette, France, with the central objective to develop a research strategy for the next 3-5 years, aiming at a systematic description of root functioning, rooting depth, and root distribution for modeling root water uptake from local and regional to global scales. The goal was to link more closely the weather prediction and climate and hydrological models with ecological and plant physiological information in order to improve the understanding of the impact that root functioning has on the hydrological cycle at various scales. The major outcome of the workshop was a number of recommendations, detailed at the end of this paper, on root water uptake parameterization and modeling and on collection of root and soil hydraulic data.

The 2019/20 Black Summer bushfire disaster in southeast Australia was unprecedented: the extensive area of forest burnt, the radiative power of the fires, and the extraordinary number of fires that developed into extreme pyroconvective events were all unmatched in the historical record. Australia’s hottest and driest year on record, 2019, was characterised by exceptionally dry fuel loads that primed the landscape to burn when exposed to dangerous fire weather and ignition. The combination of climate variability and long-term climate trends generated the climate extremes experienced in 2019, and the compounding effects of two or more modes of climate variability in their fire-promoting phases (as occurred in 2019) has historically increased the chances of large forest fires occurring in southeast Australia. Palaeoclimate evidence also demonstrates that fire-promoting phases of tropical Pacific and Indian ocean variability are now unusually frequent compared with natural variability in pre-industrial times. Indicators of forest fire danger in southeast Australia have already emerged outside of the range of historical experience, suggesting that projections made more than a decade ago that increases in climate-driven fire risk would be detectable by 2020, have indeed eventuated. The multiple climate change contributors to fire risk in southeast Australia, as well as the observed non-linear escalation of fire extent and intensity, raise the likelihood that fire events may continue to rapidly intensify in the future. Improving local and national adaptation measures while also pursuing ambitious global climate change mitigation efforts would provide the best strategy for limiting further increases in fire risk in southeast Australia.

In the Project for Intercomparison of Land-Surface Parameterization Schemes phase 2a experiment, meteorological data for the year 1987 from Cabauw, the Netherlands, were used as inputs to 23 land-surface flux schemes designed for use in climate and weather models. Schemes were evaluated by comparing their outputs with long-term measurements of surface sensible heat fluxes into the atmosphere and the ground, and of upward longwave radiation and total net radiative fluxes, and also comparing them with latent heat fluxes derived from a surface energy balance. Tuning of schemes by use of the observed flux data was not permitted. On an annual basis, the predicted surface radiative temperature exhibits a range of 2 K across schemes, consistent with the range of about 10 W m Ϫ2 in predicted surface net radiation. Most modeled values of monthly net radiation differ from the observations by less than the estimated maximum monthly observational error (Ϯ10 W m Ϫ2). However, modeled radiative surface temperature appears to have a systematic positive bias in most schemes; this might be explained by an error in assumed emissivity and by models' neglect of canopy thermal heterogeneity. Annual means of sensible and latent heat fluxes, into which net radiation is partitioned, have ranges across schemes of

A tropical deforestation experiment has been conducted in which the tropical moist forest throughout the Amazon Basin and SE Asia has been replaced by scrub grassland in a version of the National Center for Atmospheric Research Community Climate Model (Version 1), which also incorporates a mixed layer ocean and the Biosphere‐Atmosphere Transfer Scheme. In both regions we find a smaller temperature increase than did all other previous experiments except that of Henderson‐Sellers and Gornitz (1984); indeed, temperatures decrease in some months. On the other hand, we find larger runoff decreases and a larger difference between the changes in evaporation and precipitation than all earlier experiments indicating a basin‐wide decrease in moisture convergence. Disturbances in South America extend beyond the region of land‐surface change causing temperature reductions and precipitation increases to the south of the deforested Amazon. Changes to the surface climate in the deforested area take between 1 to 2 years to become fully established although the root zone soil moisture is still decreasing in year 3 and the variability of soil moisture and total cloud amount continue increasing throughout the 6‐year integration. Besides temperature and precipitation, other fields show statistically significant alterations, especially evaporation and net surface radiation (both decreased).