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Attribution of the Spring Snow Cover Extent Decline in the Northern Hemisphere, Eurasia and North America to Anthropogenic Influence

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Attribution of the Spring Snow Cover Extent Decline in the Northern Hemisphere, Eurasia and North America to Anthropogenic Influence Climatic Change (2016) 136:571–586 DOI 10.1007/s10584-016-1632-2 Attribution of the spring snow cover extent decline in the Northern Hemisphere, Eurasia and North America to anthropogenic influence Mohammad Reza Najafi1 & Francis W. Zwiers1 & Nathan P. Gillett2 Received: 13 July 2015 /Accepted: 9 February 2016 /Published online: 25 February 2016 # The Author(s) 2016. This article is published with open access at Springerlink.com Abstract While it is generally accepted that the observed reduction of the Northern Hemi- sphere spring snow cover extent (SCE) is linked to warming of the climate system caused by human induced greenhouse gas emissions, it has been difficult to robustly quantify the anthropogenic contribution to the observed change. This study addresses the challenge by undertaking a formal detection and attribution analysis of SCE changes based on several observational datasets with different structural characteristics, in order to account for the substantial observational uncertainty. The datasets considered include a blended in situ- satellite dataset extending from 1923 to 2012 (Brown), the National Oceanic and Atmospheric Administration (NOAA) snow chart Climate Data Record for 1968–2012, the Global Land Data Assimilation System version 2.0 (GLDAS-2 Noah) reanalysis for 1951–2010, and the NOAA 20th-century reanalysis, version 2 (20CR2) covering 1948–2012. We analyse observed early spring (March-April) and late spring (May-June) NH SCE extent changes in these datasets using climate simulations of the responses to anthropogenic and natural forcings combined (ALL) and to natural forcings alone (NAT) from the Coupled Model Intercompar- ison Project Phase 5 (CMIP5). The ALL-forcing response is detected in all of the observed records, indicating that observed changes are inconsistent with internal variability. The analysis also shows that the ALL-forcing simulations substantially underestimate the observed changes as recorded in the Brown and NOAA datasets, but that they are more consistent with changes seen in the GLDAS and 20CR2 reanalyses. A two-signal analysis of the GLDAS data is able to detect the influence of the anthropogenic component of the observed SCE changes Electronic supplementary material The online version of this article (doi:10.1007/s10584-016-1632-2) contains supplementary material, which is available to authorized users. * Mohammad Reza Najafi [email protected] 1 Pacific Climate Impacts Consortium, University of Victoria, Victoria, BC, Canada 2 Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, BC, Canada 572 Climatic Change (2016) 136:571–586 separately from the effect of natural forcing. Despite dataset and modelling uncertainty, these results, together with the understanding of the causes of observed warming over the past century, provide substantial evidence of a human contribution to the observed decline in Northern Hemisphere spring snow cover extent. 1 Introduction Northern Hemisphere (NH) snow cover extent (SCE) has declined over the past 90 years with the greatest reduction (~3.3 million km2) observed during the 1980s (Vaughan et al. 2013). Studies have generally focused on the spring season, when higher snow albedo feedbacks occur, including March-April (Brown and Robinson 2011; Brutel-Vuilmet et al. 2013; Rupp et al. 2013), May-June(Brown et al. 2010; Callaghan et al. 2011), April-June (Derksen and Brown 2012; Déry and Brown 2007), and March-May (Robinson and Frei 2000). Although SCE reductions would substantially affect water supply and hydroclimatic extremes (Najafi and Moradkhani 2015;Yangetal.2003), only few studies have addressed the role of anthropogenic forcing agents such as greenhouse gases on decreasing SCE trends. The IPCC Fourth Assessment Report (Hegerl et al. 2007a) noted the consistency between the decline in global SCE and warming, but did not formally attribute the observed SCE changes to human influence on the climate system. Recently, Rupp et al. (2013) detected the response to a combination of anthropogenic and natural forcings in observed SCE by comparing observed March and April SCE changes over the period 1922–2005 based on the dataset of Brown and Robinson (2011; see also Brown, 2000) with an ensemble average of CMIP5 (Coupled Model Intercomparison Project Phase 5) simulations that were driven with historical natural and anthropogenic forcings combined (ALL). They showed that while SCE in the models declined, as expected, the model-simulated trends were only half as strong as observed. They did not investigate whether the response to anthropogenic forcings (ANT) can be separated from the response to natural forcing (NAT) in observations, and also did not consider observational uncertainty, for example, as represented by differences between different datasets (Brown et al. 2010;Mudryketal.2015). Earlier Pierce et al. (2008), in a regional study of the western US, had detected anthropogenic influence on the mass of the April 1st snow pack, measured as snow water equivalent (SWE), normalized by the amount of precipitation that had been received during the preceding 6-months (i.e., the antecedent October-March period). This study, undertakes a detailed assessment of the observed and simulated SCE decline over the Northern Hemisphere, North America, and Eurasia using multiple observed datasets. We compare simulated SCE changes with observed changes in several datasets that are based on in-situ measurements, remotely sensed records, a combination of the two, and from reanalyses of various types from which it is possible to estimate observationally constrained historical SCE changes. The differences between these products underscore that there are large observational uncertainties in estimates of SCE (Vaughan et al. 2013). We therefore use multiple observational products to ensure the robustness of SCE detection and attribution results and account for observational uncertainty. It should be noted that some reanalysis datasets with relatively recent start dates, such as the Modern-Era Retrospective Analysis for Research and Applications (MERRA; (Rienecker et al. 2011)) and European Centre for Medium Range Forcecasting Interm Reanalysis (ERA-Interim; (Dee et al. 2011)), both starting in 1979, are likely too short to detect anthropogenic signals at sub-hemispheric scales and thus are not included in this study. We consider observed March-April and May-June SCE changes Climatic Change (2016) 136:571–586 573 in several datasets starting before 1979 to determine whether changes are the consequence of external forcing. 2Dataandmethods Four observational datasets based on satellite measurements, blended in-situ and satellite measurements, a global land surface data assimilation system and an atmospheric reanalysis are considered. The satellite-based weekly SCE dataset of Robinson et al. (2012) for 1968–2012 is based on NOAA’s National Climatic Data Center (NCDC) Climate Data Record (CDR). It is the longest available satellite record of NH SCE and has been used in numerous studies (Brown and Derksen 2013; Brown and Robinson 2011;DerksenandBrown2012; Déry and Brown 2007;Dye2002; Flanner et al. 2009). Both the satellite instruments and retrieval methods that underpin this record have evolved over time. From October 1966 to June 1999 SCE was estimated by visually assessing satellite shortwave imagery. Weekly SCE maps were generated on an 89 × 89 line grid overlaid on a NH stereographic map with spatial resolution ranging from 16,000 km2 to 42,000 km2 (Robinson et al. 1993). Each grid cell was flagged as snow covered if more than 50 % of its area had visible snow cover; otherwise it was categorized as snow free. The National Ice Center daily Interactive Multisensor Snow and Ice Mapping System (IMS) replaced the manual approach in June 1999 (Ramsay 1998). IMS relies on the visible satellite imagery such as MODIS, and utilizes station observations and passive microwave data to improve snow monitoring during cloudy or nighttime conditions. Resolu- tion increased from 24 km to 4 km in 2004. From June, 1999 onwards, the CDR reports pseudo-weekly charts based on the IMS data on the same 89 × 89 mesh that was used for the visually assessed product. The snow cover mapping improvements over time, along with other factors such as cloud cover changes, may have induced non-climatic temporal changes in estimated SCE as discussed in previous studies (Brown and Derksen 2013;Wangetal.2005). Using the weekly snow charts from the CDR we obtained annual March-April and May- June mean snow cover fractions (SCF) for each grid cell by calculating the fraction of the period when snow cover was present. The area of each grid cell was then multiplied by its SCF to obtain the corresponding snow cover extent. The blended insitu-satellite dataset considered in this study is that of Brown and Robinson (2011), who provide estimates of NH March-April SCE over 1923–2012 based on historical station records and the NOAA weekly satellite snow cover data described above. Pre-satellite SCE data in this dataset is based on monthly snow cover and daily snow depth observations available in the former Soviet Union, the People’s Republic of China, Canada and the United States, and on reconstructed monthly snow cover duration from daily temperature and precipitation station data. A third dataset considered in this study is based on monthly snow water equivalent (SWE) obtained from the Global Land Data Assimilation System version 2.0 (i.e. GLDAS-2 Noah) reanalysis for the
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