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A review of many studies published since the late 1920s reveals that the main driving mechanisms responsible for the early-twentieth-century Arctic warming (ETCAW) are not fully recognized. The main obstacle seems to be our limited knowledge about the climate of this period and some forcings. A deeper knowledge based on greater spatial and temporal resolution data is needed. The article provides new (or improved) knowledge about surface air temperature (SAT) conditions (including their extreme states) in the Arctic during the ETCAW. Daily and subdaily data have been used (mean daily air temperature, maximum and minimum daily temperature, and diurnal temperature range). These were taken from 10 individual years (selected from the period 1934–50) for six meteorological stations representing parts of five Arctic climatic regions. Standard SAT characteristics were analyzed (monthly, seasonal, and yearly means), as were rarely investigated aspects of SAT characteristics (e.g., number of characteristic days, day-to-day temperature variability, and the onset, end, and duration of thermal seasons). The results were compared with analogical calculations done for data taken from the contemporary Arctic warming (CAW) period (2007–16). The Arctic experienced warming between the ETCAW and the CAW. The magnitude of warming was greatest in the Pacific (2.7°C) and Canadian Arctic (1.9°C) regions. A shortening of winter and lengthening of summer were noted. Furthermore, the climate was also a little more continental (except the Russian Arctic) and less stable (greater day-to-day variability and diurnal temperature range) during the ETCAW than during the CAW. Significance Statement: It is well established that human activity (particularly increased greenhouse gas emissions) is the primary driving mechanism of the recent dramatic warming in the Arctic. However, the causes of a similar warming here in the first half of the twentieth century remain uncertain. The limited knowledge about the climate of that period—which mainly results from the low resolution of data—is a significant obstacle to a definitive determination of the forcing mechanisms. Therefore, the main aim of our paper is to improve our understanding of specific aspects of weather and climate (including extremes) using long-term series of daily and subdaily data that have rarely been applied for this purpose. This new, more comprehensive knowledge about the historical Arctic climate should allow the scientific community (particularly climate modelers) to better validate both climate models and reanalysis products and, consequently, to more precisely identify the causes of the early-twentieth-century Arctic warming. [ABSTRACT FROM AUTHOR]

The detection and attribution of climate change in the observed record play a central role in synthesizing knowledge of the climate system. Unfortunately, the traditional method for detecting and attributing changes due to multiple forcings requires large numbers of general circulation model (GCM) simulations incorporating different initial conditions and forcing scenarios, and these have only been performed with a small number of GCMs. This paper presents an extension to the fingerprinting technique that permits the inclusion of GCMs in the multisignal analysis of surface temperature even when the required families of ensembles have not been generated. This is achieved by fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings. This methodology is applied to the very large Challenge ensemble of 62 simulations of historical climate conducted with the NCAR Community Climate System Model version 1.4 (CCSM1.4) GCM, as well as some simulations from other GCMs. Considerable uncertainty exists in the estimates of the parameters in fitted EBMs. Nevertheless, temporal response patterns from these EBMs are more reliable and the combined EBM time series closely mimics the GCM in the context of transient forcing. In particular, detection and attribution results from this technique appear self-consistent and consistent with results from other methods provided that all major forcings are included in the analysis. Using this technique on the Challenge ensemble, the estimated responses to changes in greenhouse gases, tropospheric sulfate aerosols, and stratospheric volcanic aerosols are all detected in the observed record, and the responses to the greenhouse gases and tropospheric sulfate aerosols are both consistent with the observed record without a scaling of the amplitude being required. The result is that the temperature difference of the 1996–2005 decade relative to the 1940–49 decade can be attributed to greenhouse gas emissions, with a partially offsetting cooling from sulfate emissions and little contribution from natural sources. The results support the viability of the new methodology as an extension to current analysis tools for the detection and attribution of climate change, which will allow the inclusion of many more GCMs. Shortcomings remain, however, and so it should not be considered a replacement to traditional techniques. [ABSTRACT FROM AUTHOR]

This paper presents an update on the detection and attribution of global annual mean surface air temperature changes, using recently developed climate models. In particular, it applies a new methodology that permits the inclusion of many more general circulation models (GCMs) into the analysis, and it also includes more recent observations. This methodology involves fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings. Despite considerable spread in estimated EBM parameters, characteristics of model performance, such as the transient climate response, appear to be more constrained for each of the forcings. The resulting estimated response patterns are provided as input to the standard fingerprinting method used in previous studies. The estimated GCM responses to changes in greenhouse gases are detected in the observed record for all of the GCMs, and are generally found to be consistent with the observed changes; the same is generally true for the responses to changes in stratospheric aerosols from volcanic eruptions. GCM responses to changes in tropospheric sulfate aerosols and solar irradiance also appear consistent with the observed record, although the uncertainty is larger. Greenhouse gas and solar irradiance changes are found to have contributed to a best guess of ∼0.8 and ∼0.3 K warming over the 1901–2005 period, respectively, while sulfate aerosols have contributed a ∼0.4 K cooling. This analysis provides an observationally constrained estimate of future warming, which is found to be fairly robust across GCMs. By 2100, a warming of between about 1.5 and 4.5 K can be expected according to the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B emissions scenario. These results indicate an emerging constraint for global mean surface temperature responses to external forcings across GCMs, which is corroborated in the observed record. This implies that observationally constrained estimates of past warming and predictions of future warming are indeed becoming robust. [ABSTRACT FROM AUTHOR]

The framework of feedback analysis is used to explore the controls on the shape of the probability distribution of global mean surface temperature response to climate forcing. It is shown that ocean heat uptake, which delays and damps the temperature rise, can be represented as a transient negative feedback. This transient negative feedback causes the transient climate change to have a narrower probability distribution than that of the equilibrium climate response (the climate sensitivity). In this sense, climate change is much more predictable than climate sensitivity. The width of the distribution grows gradually over time, a consequence of which is that the larger the climate change being contemplated, the greater the uncertainty is about when that change will be realized. Another consequence of this slow growth is that further efforts to constrain climate sensitivity will be of very limited value for climate projections on societally relevant time scales. Finally, it is demonstrated that the effect on climate predictability of reducing uncertainty in the atmospheric feedbacks is greater than the effect of reducing uncertainty in ocean feedbacks by the same proportion. However, at least at the global scale, the total impact of uncertainty in climate feedbacks is dwarfed by the impact of uncertainty in climate forcing, which in turn is contingent on choices made about future anthropogenic emissions. [ABSTRACT FROM AUTHOR]

The temperature response to a greenhouse gas (GHG) concentration change is studied in an ocean–atmosphere coupled model—L’Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL-CM4)—for both a glacial and an interglacial context. The response to a GHG concentration changing from Last Glacial Maximum (LGM) to preindustrial values is similar for both climatic contexts in terms of temperature pattern, but the magnitude is greater under modern ones. The model simulates the classical amplification of the temperature response in the northern high latitudes compared to lower latitudes and over the land surfaces compared to the ocean. The physical reasons for the differential warming according to the latitude and to the surface type are studied through an analysis of the energy flux changes, which are decomposed to consider and quantify many different physical processes. The results highlight the role of many different factors in the thermal response to a GHG forcing for different regions, and stress, for instance, the large effect of increased water vapor concentration in the atmosphere. Concerning the land–sea warming ratio, several fluxes contribute to the final value of the ratio, with latent flux having the greatest influence. The different contributions are quantified. The comparison of the flux changes between the interglacial and glacial contexts shows that the differences are more than a simple effect of different surface emissions of the base state. It suggests that the climatic context is particularly important for the cloud and oceanic advection responses to the forcing, along with albedo effects. [ABSTRACT FROM AUTHOR]

Simulations of future climate scenarios produced with a high-resolution climate model show markedly different trends in temperature and precipitation over the Pacific Northwest than in the global model in which it is nested, apparently because of mesoscale processes not being resolved at coarse resolution. Present-day (1990–99) and future (2020–29, 2045–54, and 2090–99) conditions are simulated at high resolution (15-km grid spacing) using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) system and forced by ECHAM5 global simulations. Simulations use the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 emissions scenario, which assumes a rapid increase in greenhouse gas concentrations. The mesoscale simulations produce regional alterations in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land–water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. Warming is significantly amplified through snow–albedo feedback in regions where snow cover is lost. Increased onshore flow in the spring reduces the daytime warming along the coast. Precipitation increases in autumn are amplified over topography because of changes in the large-scale circulation and its interaction with the terrain. The robustness of the modeling results is established through comparisons with the observed and simulated seasonal variability and with statistical downscaling results. [ABSTRACT FROM AUTHOR]