Previous studies have identified a recent increase in wildfire activity in the western United States (WUS). However, the extent to which this trend is due to weather pattern changes dominated by natural variability versus anthropogenic warming has been unclear. Using an ensemble constructed flow analogue approach, we have employed observations to estimate vapor pressure deficit (VPD), the leading meteorological variable that controls wildfires, associated with different atmospheric circulation patterns. Our results show that for the period 1979 to 2020, variation in the atmospheric circulation explains, on average, only 32% of the observed VPD trend of 0.48 ± 0.25 hPa/decade (95% CI) over the WUS during the warm season (May to September). The remaining 68% of the upward VPD trend is likely due to anthropogenic warming. The ensemble simulations of climate models participating in the sixth phase of the Coupled Model Intercomparison Project suggest that anthropogenic forcing explains an even larger fraction of the observed VPD trend (88%) for the same period and region. These models and observational estimates likely provide a lower and an upper bound on the true impact of anthropogenic warming on the VPD trend over the WUS. During August 2020, when the August Complex “Gigafire” occurred in the WUS, anthropogenic warming likely explains 50% of the unprecedented high VPD anomalies.

A constellation of satellites is now in orbit providing information about terrestrial carbon and water storage and fluxes. These combined observations show that the tropical biosphere has changed significantly in the last 2 decades from the combined effects of climate variability and land use. Large areas of forest have been cleared in both wet and dry forests, increasing the source of carbon to the atmosphere. Concomitantly, tropical fire emissions have declined, at least until 2016, from changes in land-use practices and rainfall, increasing the net carbon sink. Measurements of carbon stocks and fluxes from disturbance and recovery and of vegetation photosynthesis show significant regional variability of net biosphere exchange and gross primary productivity across the tropics and are tied to seasonal and interannual changes in water fluxes and storage. Comparison of satellite based estimates of evapotranspiration, photosynthesis, and the deuterium content of water vapor with patterns of total water storage and rainfall demonstrate the presence of vegetation-atmosphere interactions and feedback mechanisms across tropical forests. However, these observations of stocks, fluxes and inferred interactions between them do not point unambiguously to either positive or negative feedbacks in carbon and water exchanges. These ambiguities highlight the need for assimilation of these new measurements with Earth System models for a consistent assessment of process interactions, along with focused field campaigns that integrate ground, aircraft and satellite measurements, to quantify the controlling carbon and water processes and their feedback mechanisms.

This study investigates the causes of pronounced low precipitation bias over Amazonia in the Community Atmosphere Model version 5 (CAM5), a common feature in many global climate models. Our analysis is based on a suite of 3-day long hindcasts starting every day at 00Z from 1997 to 2012 and an AMIP simulation for the same period. The Amazonia dry bias appears by the second day in the hindcasts and is very robust for all the seasons with the largest bias magnitude during the wet season (December–February). The bias pattern and magnitude do not change much during different dynamical wind regimes on sub-seasonal time scales. We further classify the diurnal cycle of precipitation near the LBA sites from observations and hindcasts into three convective regimes: no precipitation, late afternoon deep convection, and nighttime deep convection. CAM5 can only simulate the late afternoon convective regime and completely fails to simulate the nighttime convection, which is mostly from propagating convective systems originating from remote locations. CAM5 mainly underestimates precipitation in the late afternoon and nighttime convective regimes, which occur during ∼67% of wet season days and account for ∼75% of accumulated precipitation amount in observations. The persistent warm temperature bias and slightly higher moisture below 850 mb likely trigger deep convection too frequently, resulting in an earlier but weaker rainfall peak in the diurnal cycle. Furthermore, shallow convection may not effectively transport moisture from boundary layer to the free atmosphere, which also leads to weaker deep convection events.

This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.

The boreal summer dry season length is reported to have been increasing in the last 3 decades over the Congo rainforest, which is the second-largest rainforest in the world. In some years, the wet season in boreal autumn starts early, while in others it arrives late. The mechanism behind such a change in the wet season onset date has not been investigated yet. Using multi-satellite data sets, we discover that the variation in aerosols in the dry season plays a major role in determining the subsequent wet season onset. Dry season aerosol optical depth (AOD) influences the strength of the southern African easterly jet (AEJ-S) and, thus, the onset of the wet season. Higher AOD associated with a higher dust mass flux reduces the net downward shortwave radiation and decreases the surface temperature over the Congo rainforest region, leading to a stronger meridional temperature gradient between the rainforest and the Kalahari Desert as early as in June. The latter, in turn, strengthens the AEJ-S, sets in an early and a stronger easterly flow, and leads to a stronger equatorward convergence and an early onset of the wet season in late August to early September. The mean AOD in the dry season over the region is strongly correlated (r=0.7) with the timing of the subsequent wet season onset. Conversely, in low AOD years, the onset of the wet season over the Congo basin is delayed to mid-October.

Research is increasingly showing how drought, heat, and wildfire influence each other. Ongoing collaborations provide templates for how best to study these phenomena and plan for their future impacts.

We have developed two statistical models for extended seasonal predictions of the upper Colorado River basin (UCRB) natural streamflow during April–July: a stepwise linear regression (reduced to a simple regression with one predictor) and a neural network model. Monthly, basin-averaged soil moisture, snow water equivalent (SWE), precipitation, and the Pacific sea surface temperature (SST) are selected as potential predictors. Pacific SST predictors (PSPs) are derived from a dipole pattern over the Pacific (30°S–65°N) that is correlated with the lagging streamflow. For both models, the correlation between the hindcasted and observed streamflow exceeds 0.60 for lead times less than 4 months using soil moisture, SWE, and precipitation as predictors. This correlation is higher than that of an autoregression model (correlation ~ 0.50). Since these land surface and atmospheric variables have no statistically significant correlations with the streamflow, PSPs are then incorporated into the models. The two models have a correlation of ~0.50 using PSPs alone for lead times from 6 to 9 months, and such skills are probably associated with stronger correlation between SST and streamflow in recent decades. The similar prediction skills between the two models suggest a largely linear system between SST and streamflow. Four predictors together can further improve short-lead prediction skills (correlation ~ 0.80). Therefore, our results confirm the advantage of the Pacific SST information in predicting the UCRB streamflow with a long lead time and can provide useful climate information for water supply planning and decisions.

Although the influence of sea surface temperature (SST) forcing and large scale teleconnection on summer droughts over the United States (US) Great Plains has been suggested for decades, the underlying mechanisms are still not fully understood. Here we show a significant correlation between a low-level moisture condition over the US Southwest in spring and rainfall variability over the Great Plains in summer. Such a connection is due to the strong influence of the Southwest dryness on the zonal moisture advection to the Great Plains from spring to summer. This advection is an important contributor for the moisture deficit during spring to early summer, and so can initiate warm season drought over the Great Plains. In other words, the well documented influence of cold season Pacific SST on the Southwest rainfall in spring, and the influence of the latter on the zonal moisture advection to the Great Plains from spring to summer, allows the Pacific climate variability in winter and spring to explain over 35% of the variance of the summer precipitation over the Great Plains, more than that can be explained by the previous documented West Pacific-North America (WPNA) teleconnection forced by tropical Pacific SST in early summer. Thus, this remote land surface feedback due to the Southwest dryness can potentially improve the predictability of summer precipitation and drought onsets over the Great Plains.

Aircraft cruising near the tropopause currently benefit from the highest thermal efficiency and the least viscous (sticky) air, within the lowest 50 km of the Earth’s atmosphere. Both advantages wane in a warming climate, because atmospheric dynamic viscosity increases with temperature, in synergy with the simultaneous engine efficiency reduction. Here, skin friction drag, the dominant term for extra aviation fuel consumption in a future warming climate, is quantified by 34 climate models under a strong emissions scenario. Since 1950, the viscosity increase at cruising altitudes (∼200 hPa) reaches ∼1.5% per century, corresponding to a total drag increment of ∼0.22% per century for commercial aircraft. Meridional gradients and regional disparities exist, with low-mid latitudes experiencing greater increases in skin friction drag. The North Atlantic Corridor (NAC) is moderately affected, but its high traffic volume generates additional fuel cost of ∼3.8×10⁷ gallons annually by 2100, compared to 2010. Globally, a normal year after 2100 would consume an extra ∼4×10⁶ barrels per year. Inter model spread is <5% of the ensemble mean, due to high inter-climate model consensus for warming trends at cruising altitudes in the tropics and subtropics. Because temperature is a well simulated parameter in the IPCC archive, with only a moderate inter-model spread, the conclusions drawn here are statistically robust. Notably, additional fuel costs are likely from the increased vertical shear and related turbulence at NAC cruising altitudes. Increased flight log availability is required to confirm this apparent increasing turbulence trend.

This study uses a multivariate self‐organizing map approach to diagnose precipitation anomalies over the United States' Great Plains during the warm season (April–August) and the associated anomalous large‐scale atmospheric patterns, as represented by standardized anomalies of 500 hPa geopotential (Z500′), integrated vapor transport (IVT′), and convective inhibition index (CINi′). Circulation patterns favoring dryness identified by the method are generally consistent with those shown in previous studies, but this study provides a more comprehensive and probabilistic characterization of those that favor drought over the Southern Great Plains (SGP) and the Central Great Plains (CGP) and their temporal evolutions. Six circulation types that are associated with warm season rainfall variability over the Great Plains are identified. The SGP droughts are attributable to more frequent and persistent northern low‐southern high as well as dominant high circulation types and are connected to larger negative CINi′. In contrast, CGP droughts are attributable to more frequent and persistent western low‐eastern high, or northern high‐southern low, or dominant high patterns, and are linked to a larger negative IVT′, but not larger CINi′. Thus, these results suggest that land surface dryness and a stable atmospheric boundary layer may play a more important role over the SGP than reduced moisture transport in warm season droughts, but reduced moisture transport may play a more important role than thermodynamic stability in droughts over the CGP.

Using satellite measurements from A‐Train constellation and Global PrecipitationMeasurement mission, we investigate the relationships between the afternoon time shallow convectivetop height (CTHafternoon) and the evening time deep convective storm top height (CTHevening) and rain rate(RRevening) over the Amazon and Congo regions. We use CloudSat cloud type stratus and stratocumulus asthe shallow afternoon clouds. Our results indicate that the afternoon shallow clouds over the Congoregion are associated with suppressed and weakened evening time deep convection, whereas shallow cloudsover the Amazon region are associated with the growth of the evening time deep convection. Over the Congoregion, wefind that as CTHafternoonincreases, shallow convective rain rate in the afternoon (RRafternoon)increases. As a result, the evening time convective available potential energy (CAPE) as well as freetropospheric humidity (RH700‐300) decrease. Consequently, condensation occurring inside deep convectionreduces and CTHeveningas well as RReveningdecrease over there. Over the Amazon region, however,RRafternoondoes not vary significantly with CTHafternoon. As CTHafternoonincreases, CAPE, RH700‐300, andcondensation occurring inside deep convection increase in the evening. As a result, deep convectiveCTHeveningand RReveningincrease with CTHafternoonover the Amazon basin. These dissimilarities in theambient condition drive the shallow to deep convective evolution differently over these two rainforests.On the other hand, shallow clouds that remain shallow in the evening are associated with less CAPE andRH700‐300,RRafternoon,and RRevening. Although CAPE and RH700‐300promote deep convection to a heightcloud top height, high vertical wind shear inhibits deep convection.