Reliable subseasonal forecasts of high summer temperatures would be very valuable for society. Although state-of-the-art numerical weather prediction (NWP) models have become much better in representing the relevant sources of predictability like land and sea surface states, the subseasonal potential is not fully realized. Complexities arise because drivers depend on the state of other drivers and on interactions over multiple time scales. This study applies statistical modeling to ERA5 data, and explores how nine potential drivers, interacting on eight time scales, contribute to the subseasonal predictability of high summer temperatures in western and central Europe. Features and target temperatures are extracted with two variations of hierarchical clustering, and are fitted with a machine learning (ML) model based on random forests. Explainable AI methods show that the ML model agrees with physical understanding. Verification of the forecasts reveals that a large part of predictability comes from climate change, but that reliable and valuable subseasonal forecasts are possible in certain windows, like forecasting monthly warm anomalies with a lead time of 15 days. Contributions of each driver confirm that there is a transfer of predictability from the land and sea surface state to the atmosphere. The involved time scales depend on lead time and the forecast target. The explainable AI methods also reveal surprising driving features in sea surface temperature and 850 hPa temperature, and rank the contribution of snow cover above that of sea ice. Overall, this study demonstrates that complex statistical models, when made explainable, can complement research with NWP models, by diagnosing drivers that need further understanding and a correct numerical representation, for better future forecasts.

This study reports on the measurements of ion and refractory black carbon (rBC) concentrations in a shallow (10.96 m) ice core sample which was drilled from the field site of the East Greenland Ice Core Project (EGRIP) in July, 2016. The results provide a recent record of rBC deposition in the East Greenland ice sheet from 1990 to 2016. The annual variability in oxygen (delta O-18) and hydrogen (delta D) isotopic compositions indicated that notably warm events occurred since 2008. Peaks in rBC occurred during summer seasons, which may be attributed to the burning of biomass in boreal summer. The rBC record and analysis of historical air trajectories using the HYSPLIT model indicated that anthropogenic BC emissions from Russia, North America and Europe contributed to the majority of rBC deposition in the Greenland region, and a reduction in anthropogenic BC consumption in these areas played a dominant role in the decrease in BC concentrations since 2000. This record also suggests that the emissions from the East Asian region (China) contributed very little to the recorded BC concentrations in East Greenland ice core. The model results indicated that radiative forcing due to BC had decreased significantly since 1990, and had remained below 0.02W m(-2) since 2000.

This study reports on the measurements of ion and refractory black carbon (rBC) concentrations in a shallow (10.96 m) ice core sample which was drilled from the field site of the East Greenland Ice Core Project (EGRIP) in July, 2016. The results provide a recent record of rBC deposition in the East Greenland ice sheet from 1990 to 2016. The annual variability in oxygen (delta O-18) and hydrogen (delta D) isotopic compositions indicated that notably warm events occurred since 2008. Peaks in rBC occurred during summer seasons, which may be attributed to the burning of biomass in boreal summer. The rBC record and analysis of historical air trajectories using the HYSPLIT model indicated that anthropogenic BC emissions from Russia, North America and Europe contributed to the majority of rBC deposition in the Greenland region, and a reduction in anthropogenic BC consumption in these areas played a dominant role in the decrease in BC concentrations since 2000. This record also suggests that the emissions from the East Asian region (China) contributed very little to the recorded BC concentrations in East Greenland ice core. The model results indicated that radiative forcing due to BC had decreased significantly since 1990, and had remained below 0.02W m(-2) since 2000.

Using the daily snow cover data at 24-km resolution from the Interactive Multi-sensor Snow and Ice Mapping System snow cover analysis, this study describes the variability in Tibetan Plateau (TP) snow cover (TPSC) at multiple time scales with a focus on the intraseasonal time scale (10-90 days). TPSC demonstrates variability over a wide range of temporal scales, but the annual cycle is generally dominant. Synoptic-scale variability, seasonal variability and interannual and long-term changes make small contributions to the total daily variability in TPSC. Intraseasonal variability (ISV) is dominant over most of the central and eastern TP and explains 22-40% of the total variability and leads to obvious variations in TPSC over periods shorter than a season. The ISV of TPSC is more active in the cold season than in the warm season. Specifically, the ISV over the Changtang Plateau explains approximately 50% of the total variability of snow cover in the cold season and is even more dominant than the annual cycle. Possible influences of regional atmospheric circulations on TPSC are also examined. TPSC variability is highly correlated with regional surface air temperature (SAT) and precipitation at an intraseasonal time scale. TPSC and SAT tend to have a simultaneous relationship, while anomalous precipitation leads to subsequent TPSC variations with a lag of approximately 5 days and a positive relationship. Such relationships are the result of intraseasonal variations in regional atmospheric circulation. The anomalous adiabatic heating induced by vertical ascending motion leads to tropospheric temperature variations. Furthermore, the horizontal advection of moisture and apparent moisture sink, which are induced by anomalous moisture supply and snow evaporation anomalies, respectively, lead to anomalous moisture associated with changes in the TPSC.

The decreasing trend in rainfall in the last few decades over the Indo-Gangetic Plains of northern India as observed in ground-based observations puts increasing stress on groundwater because irrigation uses up to 70% of freshwater resources. In this work, we have analyzed the effects of extensive irrigation over the Gangetic Plains on the seasonal mean and intra-seasonal variability of the Indian summer monsoon, using a general circulation model and a very high-resolution soil moisture dataset created using extensive field observations in a state-of-the-art hydrological model. We find that the winter-time (November-March) irrigation has a positive feedback on the Indian summer monsoon through large scale circulation changes. These changes are analogous to a positive North Atlantic Oscillation (NAO) phase during winter months. The effects of the positive NAO phase persist from winter to spring through widespread changes in surface conditions over western and central Asia, which makes the pre-monsoon conditions suitable for a subsequent good monsoon over India. Winter-time irrigation also resulted in a reduction of low frequency intra-seasonal variability over the Indian region during the monsoon season. However, when irrigation is practiced throughout the year, a decrease in June-September precipitation over the Gangetic Plains, significant at 95% level, is noted as compared to the no-irrigation scenario. This decrease is attributed to the increase in local soil moisture due to irrigation, which results in a southward shift of the moisture convergence zone during the active phase of monsoon, decreasing its mean and intraseasonal variability. Interestingly, these changes show a remarkable similarity to the long-term trend in observed rainfall spatial pattern and low-frequency variability. Our results suggest that with a decline in the mean summer precipitation and stressed groundwater resources in the Gangetic Plains, the water crisis could exacerbate, with irrigation having a weakening effect on the regional monsoon.

As an important site-specific optical parameter widely used in climate models, the mass absorption efficiency (MAE) of elemental carbon (EC), varies dramatically with the source types and governs the direct radiative forcing (DRF) estimation In this study, the MAE of EC for ambient samples collected from four major emission areas in China, i.e, Beijing-Tianjin-Hebei area (BTH), Yangtze River Delta area (YRD), Sichuan Basin area (SB), and Pearl River Delta area (PRD), as well as emissions from burning of residential honeycomb briquette, firewood and rice straw were investigated by using a filter-based method. The annual mean MAE(EC) over the four major emission areas is 7.51 m(2)/g. MAE(EC) in BTH and YRD during summer appears significantly higher than MAE(EC) in other seasons, while seasonal variations of MAE(EC) in SB and PRD suggest MAE(EC) in summer and autumn is higher than that in winter and spring. MAE(EC) for samples from fossil fuels burning and biomass open-burning is 2.10 times higher than that from residential biofuel burning, which could be one of the reasons for the higher MAE(EC) values during the seasons heavily affected by fossil fuels burning and biomass open-burning (i.e., summer and autumn) than winter for the four locations. Difference between the measured and AeroCom median value of MAE(EC) may cause underestimation of DRFEC over the studied area by a factor of 0.13. Spatial and temporal variations of MAE(EC) would also result in underestimations of DRFEC to different degrees varying with seasons and areas.