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A database of surface Antarctic snow isotopic composition is constructed using available measurements, with an estimate of data quality and local variability. Although more than 1000 locations are documented, the spatial coverage remains uneven with a majority of sites located in specific areas of East Antarctica. The database is used to analyze the spatial variations in snow isotopic composition with respect to geographical characteristics (elevation, distance to the coast) and climatic features (temperature, accumulation) and with a focus on deuterium excess. The capacity of theoretical isotopic, regional, and general circulation atmospheric models (including “isotopic” models) to reproduce the observed features and assess the role of moisture advection in spatial deuterium excess fluctuations is analyzed.

Sea surface temperature (SST) anomalies in the Kuroshio Extension (KE) region have been increasingly recognized as playing a key role in shaping the extratropical air–sea interactions. However, the extent to which such extratropical SST forcing can influence large‐scale atmospheric circulation and surrounding SST remains uncertain, particularly given strong atmospheric internal variability. Here, we investigate the atmospheric response to idealized positive SST anomalies in the KE region using large‐ensemble atmospheric general circulation model experiments. The imposed forcing generates a robust sea‐level pressure anomaly over and downstream of the KE, leading to surface westerly wind anomalies extending into the subtropical North Pacific. Despite substantial atmospheric internal variability over the Aleutian Low region, these subtropical wind anomalies consistently emerge across the ensemble. Our results suggest that SST forcing in the KE region can modulate air–sea interactions not only locally but also across a broader region extending equatorward.

Since the 1970s, Atlantic Niños during boreal summer have been linked to Pacific La Niñas the following winter. Earlier studies have explained the appearance of the Atlantic-Pacific teleconnection with changing Atlantic Niño configurations. Here we find that the non-stationarity of this teleconnection can also be explained by changes in the ocean background state, without changing the Atlantic Niño configuration. Experiments with different atmospheric general circulation models are performed where the same Atlantic Niño pattern is prescribed to different global ocean background states. The 1975–1985 global mean sea surface temperature forces a Walker Circulation response and low-level convergence over the Maritime Continent, increasing the chance of triggering a La Niña-like event in the Pacific. These results suggest that ENSO-predictions could be improved in certain periods by considering tropical Atlantic variability.

The accuracy of climate projections is improved by increasing the number of samples from ensemble experiments, leading to a decrease in the confidence interval of a target climatological variable. The improvement in the accuracy depends on the degree of independence of each ensemble member in the experiments. When the members of ensemble experiments are dependent on each other, the introduction of an effective sample size (ESS) is necessary to correctly estimate the confidence interval. This study is the first attempt to estimate the ESS for precipitation as a function of the number of ensemble members, although some previous studies have investigated another type of ESS in terms of the length of simulation period. The ESS in the present study is intrinsic to the atmospheric general circulation models (AGCM) forced by the ocean boundary condition because the outputs of AGCM ensemble members are similar or dependent on each other due to the commonly used boundary condition, i.e., the distribution of sea surface temperature, sea ice concentration, and sea ice thickness. Looking at the values of ESS as a function of geographical location, those in the tropics and over the ocean are smaller than those at higher latitudes and over continents; precipitation events in areas with smaller (larger) ESS are strongly (weakly) constrained by the ocean boundary condition. The increase in temporal and spatial averaging scales for precipitation estimation leads to the decrease in the ESS, of whose trend is attributed to the spatio-temporal characteristics of the precipitation events as represented by the power spectrum and co-spectrum.

The loss of Arctic sea ice is already having profound environmental, societal, and ecological impacts locally. A highly uncertain area of scientific research, however, is whether such Arctic change has a tangible effect on weather and climate at lower latitudes. There is emerging evidence that the geographical location of sea ice loss is critically important in determining the large-scale atmospheric circulation response and associated midlatitude impacts. However, such regional dependencies have not been explored in a thorough and systematic manner. To make progress on this issue, this study analyzes ensemble simulations with an atmospheric general circulation model prescribed with sea ice loss separately in nine regions of the Arctic, to elucidate the distinct responses to regional sea ice loss. The results suggest that in some regions, sea ice loss triggers large-scale dynamical responses, whereas in other regions sea ice loss induces only local thermodynamical changes. Sea ice loss in the Barents–Kara Seas is unique in driving a weakening of the stratospheric polar vortex, followed in time by a tropospheric circulation response that resembles the North Atlantic Oscillation. For October–March, the largest spatial-scale responses are driven by sea ice loss in the Barents–Kara Seas and the Sea of Okhotsk; however, different regions assume greater importance in other seasons. The atmosphere responds very differently to regional sea ice losses than to pan-Arctic sea ice loss, and the response to pan-Arctic sea ice loss cannot be obtained by the linear addition of the responses to regional sea ice losses. The results imply that diversity in past studies of the simulated response to Arctic sea ice loss can be partly explained by the different spatial patterns of sea ice loss imposed.

The progress made fi＇om Phase 3 to Phase 5 of the Coupled Model Intercomparison Project （CMIP3 to CMIP5） in simulating spring persistent rainfall （SPR） over East Asia was examined from the outputs of nine atmospheric general circulation models （AGCMs）. The majority of the models overestimated the precipitation over the SPR domain, with the mean latitude of the SPR belt shifting to the north. The overestimation was about 1mm d-1 in the CMIP3 ensemble, and the northward displacement was about 3°, while in the CMIP5 ensemble the overestimation was suppressed to 0.7 mm d-i and the northward shift decreased to 2.5°. The SPR features a northeast-southwest extended rain belt with a slope of 0.4°N/°E. The CMIP5 ensemble yielded a smaller slope （0.2°N/°E）, whereas the CMIP3 ensemble featured an unre- alistic zonally-distributed slope. The CMIP5 models also showed better skill in simulating the interannual variability of SPR. Previous studies have suggested that the zonal land-sea thermal contrast and sensible heat flux over the southeastern Tibetan Plateau are important for the existence of SPR. These two ther- mal factors were captured well in the CMIP5 ensemble, but underestimated in the CMIP3 ensemble. The variability of zonal land-sea thermal contrast is positively correlated with the rainfall amount over the main SPR center, but it was found that an overestimated thermal contrast between East Asia and South China Sea is a common problem in most of the CMIP3 and CMIP5 models. Simulation of the meridional thermal contrast is therefore important for the future improvement of current AGCMs.

Arctic sea ice is declining at an increasing rate with potentially important repercussions. To understand better the atmospheric changes that may have occurred in response to Arctic sea ice loss, this study presents results from atmospheric general circulation model (AGCM) experiments in which the only time-varying forcings prescribed were observed variations in Arctic sea ice and accompanying changes in Arctic sea surface temperatures from 1979 to 2009. Two independent AGCMs are utilized in order to assess the robustness of the response across different models. The results suggest that the atmospheric impacts of Arctic sea ice loss have been manifested most strongly within the maritime and coastal Arctic and in the lowermost atmosphere. Sea ice loss has driven increased energy transfer from the ocean to the atmosphere, enhanced warming and moistening of the lower troposphere, decreased the strength of the surface temperature inversion, and increased lower-tropospheric thickness; all of these changes are most pronounced in autumn and early winter (September–December). The early winter (November–December) atmospheric circulation response resembles the negative phase of the North Atlantic Oscillation (NAO); however, the NAO-type response is quite weak and is often masked by intrinsic (unforced) atmospheric variability. Some evidence of a late winter (March–April) polar stratospheric cooling response to sea ice loss is also found, which may have important implications for polar stratospheric ozone concentrations. The attribution and quantification of other aspects of the possible atmospheric response are hindered by model sensitivities and large intrinsic variability. The potential remote responses to Arctic sea ice change are currently hard to confirm and remain uncertain.

Annual precipitation anomalies over eastern China are characterized by a north–south dipole pattern, referred to as the “southern flooding and northern drought” pattern (SF/ND), fluctuating on decadal time scales. Previous research has suggested possible links with oceanic forcing, but the underlying physical mechanisms by which sea surface temperature (SST) variability impacts the dipole pattern remains unclear. Idealized atmospheric general circulation model experiments conducted by the U.S. CLIVAR Drought Working Group are used to investigate the role of historical SST anomalies associated with Pacific El Niño–Southern Oscillation (ENSO)-like and the Atlantic multidecadal oscillation (AMO) patterns in this dipole pattern. The results show that the Pacific SST pattern plays a dominant role in driving the decadal variability of this dipole pattern and the associated atmospheric circulation anomalies, whereas the Atlantic SST pattern contributes to a much lesser degree. The direct atmospheric response to the Pacific SST pattern is a large-scale cyclonic or anticyclonic circulation anomaly in the lower troposphere occupying the entire northern North Pacific. During the warm phase of the Pacific SST pattern, it is cyclonic with northwesterly wind anomalies over northern China pushing the monsoon front to the south and consequently SF/ND. During the cold phase of the Pacific SST pattern, the circulation anomaly reverses with southeasterly winds over northern China allowing the monsoon front and the associated rainband to migrate northward, resulting in southern drought and northern flooding. The Atlantic SST pattern plays a supplementary role, enhancing the dipole pattern when the Pacific SST and Atlantic SST patterns are in opposite phases and weakening it when the phases are the same.

The climatology and interannual variability of the East Asian summer monsoon (EASM) simulated by 34 coupled general circulation models (CGCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are evaluated. To estimate the role of air–sea coupling, 17 CGCMs are compared to their corresponding atmospheric general circulation models (AGCMs). The climatological low-level monsoon circulation and mei-yu/changma/baiu rainfall band are improved in CGCMs from AGCMs. The improvement is at the cost of the local cold sea surface temperature (SST) biases in CGCMs, since they decrease the surface evaporation and enhance the circulation. The interannual EASM pattern is evaluated by a skill formula and the highest/lowest eight models are selected to investigate the skill origins. The observed Indian Ocean (IO) warming, tropical eastern Indian Ocean (TEIO) rainfall anomalies, and Kelvin wave response are captured well in high-skill models, while these features are not present in low-skill models. Further, the differences in the IO warming between high-skill and low-skill models are rooted in the preceding ENSO simulation. Hence, the IO–western Pacific anticyclone (WPAC) teleconnection is important for CGCMs, similar to AGCMs. However, compared to AGCMs, the TEIO SST anomaly is warmer in CGCMs, since the easterly wind anomalies in the southern flank of the WPAC reduce the climatological monsoon westerlies and decrease the surface evaporation. The warmer TEIO induces the stronger precipitation anomaly and intensifies the teleconnection. Hence, the interannual EASM pattern is better simulated in CGCMs than that in AGCMs.

Northern midlatitudes, over central Eurasia in particular, have experienced frequent severe winters in recent decades1–3. A remote influence of Arctic sea-ice loss has been suggested4–14; however, the importance of this connection remains controversial because of discrepancies among modelling and between modelling and observational studies15–17. Here, using a hybrid analysis of observations and multi-model large ensembles from seven atmospheric general circulation models, we examine the cause of these differences. While all models capture the observed structure of the forced surface temperature response to sea-ice loss in the Barents–Kara Seas—including Eurasian cooling—we show that its magnitude is systematically underestimated. Owing to the varying degrees of this underestimation of sea-ice-forced signal, the signal-to-noise ratio differs markedly. Correcting this underestimation reconciles the discrepancy between models and observations, leading to the conclusion that ~44% of the central Eurasian cooling trend for 1995–2014 is attributable to sea-ice loss in the Barents–Kara Seas. Our results strongly suggest that anthropogenic forcing has significantly amplified the probability of severe winter occurrence in central Eurasia via enhanced melting of the Barents–Kara sea ice. The difference in underestimation of signal-to-noise ratio between models therefore calls for careful experimental design and interpretation for regional climate change attribution.The connections between Arctic sea-ice loss and severe Eurasian winters are complicated by differences among studies. Correcting model underestimates reveals that 44% of the central Eurasian cooling trend is attributable to sea-ice loss in the Barents–Kara Seas.