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The local weather and climate of the Himalayas are sensitive and interlinked with global-scale changes in climate, as the hydrology of this region is mainly governed by snow and glaciers. There are clear and strong indicators of climate change reported for the Himalayas, particularly the Jammu and Kashmir region situated in the western Himalayas. In this study, using observational data, detailed characteristics of long- and short-term as well as localized variations in temperature and precipitation are analyzed for these six meteorological stations, namely, Gulmarg, Pahalgam, Kokarnag, Qazigund, Kupwara and Srinagar during 1980–2016. All of these stations are located in Jammu and Kashmir, India. In addition to analysis of stations observations, we also utilized the dynamical downscaled simulations of WRF model and ERA-Interim (ERA-I) data for the study period. The annual and seasonal temperature and precipitation changes were analyzed by carrying out Mann–Kendall, linear regression, cumulative deviation and Student's t statistical tests. The results show an increase of 0.8 ∘C in average annual temperature over 37 years (from 1980 to 2016) with higher increase in maximum temperature (0.97 ∘C) compared to minimum temperature (0.76 ∘C). Analyses of annual mean temperature at all the stations reveal that the high-altitude stations of Pahalgam (1.13 ∘C) and Gulmarg (1.04 ∘C) exhibit a steep increase and statistically significant trends. The overall precipitation and temperature patterns in the valley show significant decreases and increases in the annual rainfall and temperature respectively. Seasonal analyses show significant increasing trends in the winter and spring temperatures at all stations, with prominent decreases in spring precipitation. In the present study, the observed long-term trends in temperature (∘Cyear-1) and precipitation (mm year−1) along with their respective standard errors during 1980–2016 are as follows: (i) 0.05 (0.01) and −16.7 (6.3) for Gulmarg, (ii) 0.04 (0.01) and −6.6 (2.9) for Srinagar, (iii) 0.04 (0.01) and −0.69 (4.79) for Kokarnag, (iv) 0.04 (0.01) and −0.13 (3.95) for Pahalgam, (v) 0.034 (0.01) and −5.5 (3.6) for Kupwara, and (vi) 0.01 (0.01) and −7.96 (4.5) for Qazigund. The present study also reveals that variation in temperature and precipitation during winter (December–March) has a close association with the North Atlantic Oscillation (NAO). Further, the observed temperature data (monthly averaged data for 1980–2016) at all the stations show a good correlation of 0.86 with the results of WRF and therefore the model downscaled simulations are considered a valid scientific tool for the studies of climate change in this region. Though the correlation between WRF model and observed precipitation is significantly strong, the WRF model significantly underestimates the rainfall amount, which necessitates the need for the sensitivity study of the model using the various microphysical parameterization schemes. The potential vorticities in the upper troposphere are obtained from ERA-I over the Jammu and Kashmir region and indicate that the extreme weather event of September 2014 occurred due to breaking of intense atmospheric Rossby wave activity over Kashmir. As the wave could transport a large amount of water vapor from both the Bay of Bengal and Arabian Sea and dump them over the Kashmir region through wave breaking, it probably resulted in the historical devastating flooding of the whole Kashmir valley in the first week of September 2014. This was accompanied by extreme rainfall events measuring more than 620 mm in some parts of the Pir Panjal range in the south Kashmir.

Climate variability, particularly that of the annual air temperature and rainfall, has received a great deal of attention worldwide. The magnitude of the variability or fluctuations of the factors varies according to locations. Hence, examining the spatiotemporal dynamics of meteorological variables in the context of changing climate, particularly in countries where rainfed agriculture is predominant, is vital to assess climate‐induced changes and suggest feasible adaptation strategies. To that end, the present study examines long‐term changes and short‐term fluctuations in monsoonal rainfall and temperature over Kalahandi, Bolangir and Koraput (hereafter KBK) districts in the state of Odisha. Both rainfall and temperature data for period of 1980–2017 were analyzed in this study. Statistical trend analysis techniques namely Mann–Kendall test and Sen's slope estimator were used to examine and analyze the problems. The detailed analysis of the data for 37 years indicate that the annual maximum temperature and annual minimum temperature have shown an increasing trend, whereas the monsoon's maximum and minimum temperatures have shown a decreasing trend. Statistically significant trends are detected for rainfall and also the result is statistically significant at 99% confidence limit during the period of 1980–2017. Rainfall is showing a quite good increasing trend (Sen's slope = 4.034) for JJAS season. In the case of maximum temperature for the observed period, it showed a slight warming or increasing trend (Sen's slope = 0.29) while the minimum temperature trend showed a cooling trend (Sen's slope = −0.006) but result of maximum temperature trend analysis is statistically significant at 95% confidence limit, on the contrary, the trend analysis result of minimum temperature is not statistically significant. Location map of Kalahandi, Bolangir and Koraput (KBK) districts in Odisha.

We present a significantly improved data‐driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid. New developments in this framework include an off‐line volume‐conservative mapping to a cubed‐sphere grid, improvements to the CNN architecture and the minimization of the loss function over multiple steps in a prediction sequence. The cubed‐sphere remapping minimizes the distortion on the cube faces on which convolution operations are performed and provides natural boundary conditions for padding in the CNN. Our improved model produces weather forecasts that are indefinitely stable and produce realistic weather patterns at lead times of several weeks and longer. For short‐ to medium‐range forecasting, our model significantly outperforms persistence, climatology, and a coarse‐resolution dynamical numerical weather prediction (NWP) model. Unsurprisingly, our forecasts are worse than those from a high‐resolution state‐of‐the‐art operational NWP system. Our data‐driven model is able to learn to forecast complex surface temperature patterns from few input atmospheric state variables. On annual time scales, our model produces a realistic seasonal cycle driven solely by the prescribed variation in top‐of‐atmosphere solar forcing. Although it currently does not compete with operational weather forecasting models, our data‐driven CNN executes much faster than those models, suggesting that machine learning could prove to be a valuable tool for large‐ensemble forecasting. Plain Language Summary Recent work has begun to explore building global weather prediction models using only machine learning techniques trained on large amounts of atmospheric data. We develop a vastly improved machine learning algorithm capable of operating like traditional weather models and predicting several fundamental atmospheric variables, including near‐surface temperature. While our model does not yet compete with the state‐of‐the‐art in numerical weather prediction, it computes realistic forecasts that perform well and execute extremely quickly, offering a potential avenue for future developments in probabilistic weather forecasting. Key Points A convolutional neural net (CNN) is developed for global weather forecasts on the cubed sphere Our CNN produces skillful global forecasts of key atmospheric variables at lead times up to 7 days Our CNN computes stable 1‐year simulations of realistic atmospheric states in 3 seconds

The magnitude of global warming is controlled by climate feedbacks associated with various aspects of the climate system, such as clouds. The global climate feedback is the net effect of these feedbacks, and its temporal evolution is thought to depend on the tropical Pacific sea surface temperature pattern. However, current coupled climate models fail to simulate the pattern observed in the Pacific between 1979 and 2013 and its associated anomalously negative feedback. Here we demonstrate a mechanism whereby the Southern Ocean controls the global climate feedback. Using climate model experiments in which Southern Ocean sea surface temperatures are restored to observations, we show that accounting for recent Southern Ocean cooling—which is absent in coupled climate models—halves the bias in the global climate feedback by removing the cloud component bias. This global impact is mediated by a teleconnection to the Southeast Pacific, where remote sea surface temperature anomalies cause a strong stratocumulus cloud feedback. We propose that this Southern Ocean-driven pattern effect is underestimated in most climate models, owing to an overly weak stratocumulus cloud feedback. Addressing this bias may shift climate sensitivities to higher values than currently simulated as the Southern Ocean undergoes accelerated warming in future projections.The temporal evolution of the net global climate feedback in recent decades has been governed by sea surface temperature patterns in the Southern Ocean, according to climate model simulations.

The magnitude of global surface temperature change in response to unit radiative forcing depends on the type and magnitude of forcing agent—a concept known as a “forcing efficacy.” However, the mechanisms behind the forcing efficacy are still unclear. In this study, we perform a set of simulations using CESM1 to calculate the efficacy of 10 different forcing agents defined in terms of fixed‐SST effective radiative forcing, and then use a Green's function approach to show that each forcing efficacy can be largely understood in terms of the radiative feedbacks associated with the different surface temperature patterns induced by the forcing agents (a pattern effect). We also quantify how the state dependence of feedbacks on global mean surface temperature anomalies impacts forcing efficacies. The results show that the forcing efficacy can be well reconstructed with a combination of pattern effect and state dependence. Plain Language Summary The magnitude of global warming in response to unit forcing induced by carbon dioxide is different to that induced by methane or solar radiation, and the efficacy of a specific climate forcing agent depends on its type and magnitude. Our results show that the forcing efficacy can be explained with a combination of pattern effect and state dependence. When there is relatively stronger forcing over the tropical western Pacific Ocean, where feedbacks are more negative, the corresponding sea surface warming pattern favors a lower efficacy. When the forcing induces a larger global surface warming, less‐stabilizing feedbacks are induced and the corresponding efficacy tends to be higher. Key Points Forcing efficacy can be explained with a combination of pattern effect and state dependence of feedbacks Efficacy is lower when there is relatively stronger forcing over the tropical western Pacific Ocean Efficacy is higher when the forcing is more positive, inducing less‐stabilizing feedbacks at higher warming

In 2023, the world experienced its highest ever global mean surface temperature (GMST). Our study underscores the pivotal significance of El Niño and sea surface temperature (SST) warming as the fundamental causes. Interannually, the increment of GMST in 2023 comprised two phases: first, gradual ocean warming associated with El Niño and the North Atlantic from January to August; second, a continued rise in land temperatures in the mid‐to‐high latitude regions from September onwards, influenced by SST patterns. Notably, the maturation of El Niño prolonged warming in North America through excitation of the Pacific‐North American teleconnection. During the most recent 15 years, GMST has entered an accelerated warming period, primarily driven by rapid SST warming trends in the tropical Indian Ocean, tropical Atlantic, subtropical North Pacific, and North Atlantic. These decadal warming patterns, combined with El Niño, may further increase GMST, with 2023 as a particularly striking example. Plain Language Summary In 2023, the world experienced its highest recorded surface temperatures. This study found that El Niño and sea surface temperature pattern effects played a significant role. The warming in 2023 had two phases: from January to August, the sea surface temperature gradually warmed, largely due to the development of El Niño and warming in the Atlantic; from September onward, land temperatures in the mid‐to‐high latitude regions sharply increased, mainly because of the warmer oceans. El Niño prolonged the warming in North America through the Pacific‐North American teleconnection. In the past decade or so, global warming trend has accelerated, mainly due to rapid warming trends in the tropical Indian Ocean, Atlantic, and subtropical North Pacific. These long‐term warming trends, combined with the 2023 El Niño event, led to the record‐breaking temperatures. Key Points Global mean surface temperatures set a new record high in 2023, significantly influenced by El Niño and sea surface warming There were two warming phases in 2023: ocean warming early, then land temperature warming later Recent rapid ocean warming has accelerated global temperature increases, with 2023 being a record year

Atmosphere‐ocean general circulation models (AOGCMs) struggle to reproduce recently observed sea surface temperature (SST) trend patterns. Here, we quantify the relevance of this SST pattern uncertainty to global‐mean temperature projections through convolving Green's functions with SST pattern scenarios that differ from the ones AOGCMs produce by themselves. We find that future SST pattern uncertainty has a significant impact on projections, such as increasing total model uncertainty by 40% in a high‐emissions scenario by 2085. A reversal of the current cooling trend in the East Pacific over the next few decades could lead to a period of global‐mean warming with a 60% higher rate than currently projected. SST pattern uncertainty works through a destabilization of the shortwave cloud feedback to affect temperature projections. It is critical for climate change impact, adaptation, and mitigation assessments to incorporate this previously unaccounted for uncertainty until we trust the evolution of SST patterns in AOGCMs. Plain Language Summary Temperature projections from climate models guide global adaptation and mitigation efforts in response to anthropogenic climate change. However, these climate models are unable to reproduce the pattern of recently observed sea surface temperature (SST) trends in large regions. In this study, we demonstrate how this discrepancy between observations and models, which we define as SST pattern uncertainty, impacts future global‐mean temperature projections. We find that SST pattern uncertainty significantly impacts projections. In fact, when the current cooling SST trend in the East Pacific switches to a warming trend, the planet could experience a period of strong warming with a warming rate 60% greater than what current projections from climate models suggest. We recommend this uncertainty be incorporated into future climate change assessments until we thoroughly trust the pattern of SST evolution in climate models. Key Points Since climate models struggle to reproduce the recently observed sea surface temperature trend pattern, they may continue doing so in the future Accounting for this error in temperature projections increases uncertainty by as much as 40% in a high emissions scenario by 2085 A reversal of the cooling trend in the East Pacific could lead to a warming rate 60% higher than current projections

Wintertime temperature extremes sometimes show a continental linkage between Eurasia and North America (NA), but whether these connections are coincidental or dynamically robust remains unclear. This study investigates the linkages of the leading intraseasonal temperature patterns between Eurasia and NA, focusing on the underlying dynamic processes. Our findings reveal a weak but robust linkage between the dominant patterns in both regions. Specifically, an opposite‐phase temperature anomaly in NA occurs about 1 week after a Eurasian temperature anomaly, influenced by wave propagation in both the troposphere and stratosphere. Conversely, a same‐phase temperature anomaly appears over central Eurasia approximately 1 week after a North American temperature anomaly, primarily driven by a Scandinavian‐like pattern in the troposphere. These relationships sometimes overlap, forming a sequence of temperature changes across mid‐high latitudes, closely tied to the stratosphere‐troposphere coupling process. The findings provide new insights for a more comprehensive understanding of wintertime intraseasonal temperature variability. Plain Language Summary Both severe cold and warm extremes occur frequently in winter, especially over Eurasia and North America (NA). More importantly, winter temperature extremes sometimes occur simultaneously over the two continents. Such spatially compounded extremes can cause serious socio‐economic losses, highlighting the need for a better comprehensive understanding of winter temperature variability in the midlatitudes. In this work, we found that the temperature intraseasonal variabilities over Eurasia and NA are dynamically linked, with temperature anomalies on one continent closely following the other. We further explained the reasons for this link, which involves large‐scale atmospheric wave trains in both the troposphere and the stratosphere. Key Points The leading intraseasonal patterns of winter surface air temperature anomaly over Eurasia and North America (NA) show a weak but robust linkage Eurasia precedes an opposite‐phase anomaly in NA, bridged by a tropospheric wave train and wave reflection from the stratosphere NA precedes a same‐phase anomaly in Eurasia, mainly driven by a Scandinavian‐like pattern in the troposphere

Previous research has shown that stratospheric aerosol causes only a small temperature change per unit forcing because they produce stronger cooling in the tropical Indian Ocean and the western Pacific Ocean than in the global mean. The enhanced temperature change in this so-called “warm-pool” region activates strongly negative local and remote feedbacks, which dampen the global mean temperature response. This paper addresses the question of why stratospheric aerosol forcing affects warm-pool temperatures more strongly than CO2 forcing, using idealized MPI-ESM simulations. We show that the aerosol's enhanced effective forcing at the top of the atmosphere (TOA) over the warm pool contributes to the warm-pool-intensified temperature change but is not sufficient to explain the effect. Instead, the pattern of surface effective forcing, which is substantially different from the effective forcing at the TOA, is more closely linked to the temperature pattern. Independent of surface temperature changes, the aerosol heats the tropical stratosphere, accelerating the Brewer–Dobson circulation. The intensified Brewer–Dobson circulation exports additional energy from the tropics to the extratropics, which leads to a particularly strong negative forcing at the tropical surface. These results show how forced circulation changes can affect the climate response by altering the surface forcing pattern. Furthermore, they indicate that the established approach of diagnosing effective forcing at the TOA is useful for global means, but a surface perspective on the forcing must be adopted to understand the evolution of temperature patterns.

The sensitivity of the radiative flux at the top of the atmosphere to surface temperature perturbations cannot be directly observed. The relationship between sea surface temperature (SST) and top‐of‐atmosphere radiation can be estimated with Green's function (GF) simulations by locally perturbing the sea surface temperature boundary conditions in atmospheric climate models. We perform such simulations with the Ai2 Climate Emulator (ACE), a machine learning‐based emulator trained on ERA5 reanalysis data (ACE2‐ERA5). This produces a sensitivity map of the top‐of‐atmosphere radiative response to surface warming that aligns with our physical understanding of radiative feedbacks. However, ACE2‐ERA5 likely underestimates the radiative response to historical warming. We compare to two additional versions of ACE and traditional climate models. We argue that GF experiments can be used to evaluate the performance and limitations of machine learning‐based climate emulators by examining if causal physical relationships are correctly represented and testing their capability for out‐of‐distribution predictions.