Evidence for spring mountain snowpack retreat from a Landsat-derived snow cover climate data record

Abstract. A Landsat snow cover climate data record (CDR) of visible mountain snow-covered area (SCA) across interior northwestern USA during spring was compared with ground-based snow telemetry (SNOTEL) snow-water-equivalent (SWE) measurements and mean surface temperature and total precipitation observations. Landsat spring SCA on 1 June was positively correlated with 15 May and 1 June SWE, negatively correlated with spring temperatures (April–June), and positively correlated with March precipitation. Using linear regression with predicted residual error sum-of-squares (PRESS) cross-validation, spring SCA was reconstructed (1901–2009) for the mountains of central Idaho and southwestern Montana using instrumental spring surface temperature records. The spring SCA reconstruction shows natural internal variability at interannual to decadal timescales including above average SCA in the 1900s, 1910s, 1940s-1970s, and below average SCA in the 1920s, 1930s, and since the mid 1980s. The reconstruction also reveals a~centennial trend towards decreasing spring SCA with estimated losses of −36.2 % since 1901. Based on the inferred thermal relationship between temperature and snow, strong evidence emerges for mountain snowpack retreat triggered by spring warming, a signal that includes both feedback and response mechanisms. Expanding snow cover CDRs to include additional operational satellite retrievals will add temporal SCA estimates during other snow accumulation and melt intervals for improved satellite-instrumental climate model calibration. Merging Landsat snow cover CDRs with instrumental climate records is a formidable method to monitor climate-driven changes in western US snowpack extent over 20th and 21st centuries.

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The AMSU-Based Hydrological Bundle Climate Data Record—Description and Comparison with Other Data Sets

Remote Sensing ◽

10.3390/rs10101640 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1640 ◽

Cited By ~ 3

Author(s):

Ralph Ferraro ◽

Brian Nelson ◽

Tom Smith ◽

Olivier Prat

Keyword(s):

Land Surface ◽

Snow Water Equivalent ◽

Precipitable Water ◽

Climate Data ◽

Sea Ice Concentration ◽

Data Set ◽

Data Record ◽

Microwave Imager ◽

Global Precipitation ◽

Climate Data Record

Passive microwave measurements have been available on satellites back to the 1970s, first flown on research satellites developed by the National Aeronautics and Space Administration (NASA). Since then, several other sensors have been flown to retrieve hydrological products for both operational weather applications (e.g., the Special Sensor Microwave/Imager—SSM/I; the Advanced Microwave Sounding Unit—AMSU) and climate applications (e.g., the Advanced Microwave Scanning Radiometer—AMSR; the Tropical Rainfall Measurement Mission Microwave Imager—TMI; the Global Precipitation Mission Microwave Imager—GMI). Here, the focus is on measurements from the AMSU-A, AMSU-B, and Microwave Humidity Sounder (MHS). These sensors have been in operation since 1998, with the launch of NOAA-15, and are also on board NOAA-16, -17, -18, -19, and the MetOp-A and -B satellites. A data set called the “Hydrological Bundle” is a climate data record (CDR) that utilizes brightness temperatures from fundamental CDRs (FCDRs) to generate thematic CDRs (TCDRs). The TCDRs include total precipitable water (TPW), cloud liquid water (CLW), sea-ice concentration (SIC), land surface temperature (LST), land surface emissivity (LSE) for 23, 31, 50 GHz, rain rate (RR), snow cover (SC), ice water path (IWP), and snow water equivalent (SWE). The TCDRs are shown to be in general good agreement with similar products from other sources, such as the Global Precipitation Climatology Project (GPCP) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). Due to the careful intercalibration of the FCDRs, little bias is found among the different TCDRs produced from individual NOAA and MetOp satellites, except for normal diurnal cycle differences.

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Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

Remote Sensing ◽

10.3390/rs12162554 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2554

Author(s):

Christopher J. Merchant ◽

Owen Embury

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Large Scale ◽

Dust Aerosol ◽

Sea Surface ◽

Climate Data ◽

Desert Dust ◽

Data Record ◽

Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

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Satellite-based time-series of sea-surface temperature since 1981 for climate applications

Scientific Data ◽

10.1038/s41597-019-0236-x ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 27

Author(s):

Christopher J. Merchant ◽

Owen Embury ◽

Claire E. Bulgin ◽

Thomas Block ◽

Gary K. Corlett ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Ocean Model ◽

Sea Surface ◽

Climate Data ◽

Satellite Measurements ◽

Data Record ◽

The Mean ◽

Climate Data Record

Abstract A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

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The Cloud_cci simulator for the ESA Cloud_cci climate data record and its application to a global and a regional climate model

10.5194/gmd-2018-212 ◽

2018 ◽

Author(s):

Salomon Eliasson ◽

Karl Göran Karlsson ◽

Erik van Meijgaard ◽

Jan Fokke Meirink ◽

Martin Stengel ◽

...

Keyword(s):

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Cloud Fraction ◽

Climate Data ◽

European Continent ◽

Cloud Properties ◽

Data Record ◽

The Impact ◽

Climate Data Record

Abstract. The Cloud&lowbar;cci satellite simulator has been developed to enable comparisons between the Cloud&lowbar;cci Climate Data Record (CDR) and climate models. The Cloud&lowbar;cci simulator is applied here to the EC-Earth Global Climate Model as well as the RACMO Regional Climate Model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in Liquid Water Path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud&lowbar;cci CDR's lack of sensitivity to very thin clouds. This reduces Total Cloud Fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian sub-continent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean, but lower than retrieved over the European continent, where in addition the Cloud Top Pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modelled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

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Climate Data to Undertake Hygrothermal and Whole Building Simulations Under Projected Climate Change Influences for 11 Canadian Cities

Data ◽

10.3390/data4020072 ◽

2019 ◽

Vol 4 (2) ◽

pp. 72 ◽

Cited By ~ 4

Author(s):

Abhishek Gaur ◽

Michael Lacasse ◽

Marianne Armstrong

Keyword(s):

Climate Change ◽

Snow Cover ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Internal Variability ◽

Climatic Conditions ◽

Climate Data ◽

Wide Range ◽

The Future

Buildings and homes in Canada will be exposed to unprecedented climatic conditions in the future as a consequence of global climate change. To improve the climate resiliency of existing and new buildings, it is important to evaluate their performance over current and projected future climates. Hygrothermal and whole building simulation models, which are important tools for assessing performance, require continuous climate records at high temporal frequencies of a wide range of climate variables for input into the kinds of models that relate to solar radiation, cloud-cover, wind, humidity, rainfall, temperature, and snow-cover. In this study, climate data that can be used to assess the performance of building envelopes under current and projected future climates, concurrent with 2 °C and 3.5 °C increases in global temperatures, are generated for 11 major Canadian cities. The datasets capture the internal variability of the climate as they are comprised of 15 realizations of the future climate generated by dynamically downscaling future projections from the CanESM2 global climate model and thereafter bias-corrected with reference to observations. An assessment of the bias-corrected projections suggests, as a consequence of global warming, future increases in the temperatures and precipitation, and decreases in the snow-cover and wind-speed for all cities.

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Construction of a climate data record of sea surface temperature from passive microwave measurements

Remote Sensing of Environment ◽

10.1016/j.rse.2019.111485 ◽

2020 ◽

Vol 236 ◽

pp. 111485 ◽

Cited By ~ 2

Author(s):

Emy Alerskans ◽

Jacob L. Høyer ◽

Chelle L. Gentemann ◽

Leif Toudal Pedersen ◽

Pia Nielsen-Englyst ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Passive Microwave ◽

Sea Surface ◽

Microwave Measurements ◽

Climate Data ◽

Data Record ◽

Climate Data Record

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The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model

Geoscientific Model Development ◽

10.5194/gmd-12-829-2019 ◽

2019 ◽

Vol 12 (2) ◽

pp. 829-847 ◽

Cited By ~ 3

Author(s):

Salomon Eliasson ◽

Karl Göran Karlsson ◽

Erik van Meijgaard ◽

Jan Fokke Meirink ◽

Martin Stengel ◽

...

Keyword(s):

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Cloud Fraction ◽

Climate Data ◽

European Continent ◽

Cloud Properties ◽

Data Record ◽

The Impact ◽

Climate Data Record

Abstract. The Cloud Climate Change Initiative (Cloud_cci) satellite simulator has been developed to enable comparisons between the Cloud_cci climate data record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth global climate model as well as the Regional Atmospheric Climate Model (RACMO) regional climate model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in liquid water path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces total cloud fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian subcontinent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean but lower than retrieved over the European continent, where in addition the cloud top pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modeled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

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A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring

Earth System Science Data Discussions ◽

10.5194/essdd-7-669-2014 ◽

2014 ◽

Vol 7 (2) ◽

pp. 669-691 ◽

Cited By ~ 2

Author(s):

T. W. Estilow ◽

A. H. Young ◽

D. A. Robinson

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Environmental Data ◽

Climatic Data ◽

Climate Data ◽

Snow Cover Extent ◽

Data Record ◽

Climate Data Record ◽

Climate Studies

Abstract. This paper describes the long-term, satellite-based visible snow cover extent NOAA climate data record (CDR) currently available for climate studies, monitoring, and model validation. This environmental data product is developed from weekly Northern Hemisphere snow cover extent data that have been digitized from snow cover maps onto a Cartesian grid draped over a polar stereographic projection. The data has a spatial resolution of 190.5 km at 60 ° latitude, are updated monthly, and span from 4 October 1966 to present. The data comprise the longest satellite-based CDR of any environmental variable. Access to the data are provided in netCDF format and are archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (doi:10.7289/V5N014G9). The basic characteristics, history, and evolution of the dataset are presented herein. In general, the CDR provides similar spatial and temporal variability as its widely used predecessor product. Key refinements to the new CDR improve the product's grid accuracy and documentation, and bring metadata into compliance with current standards for climate data records.

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A regional climate model hindcast for Siberia – assessing the added value of snow water equivalent using ESA GlobSnow and reanalyses

The Cryosphere Discussions ◽

10.5194/tcd-6-4637-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 4637-4671

Author(s):

K. Klehmet ◽

B. Geyer ◽

B. Rockel

Keyword(s):

Regional Climate Model ◽

Snow Cover ◽

Large Scale ◽

Snow Water Equivalent ◽

Climate Model ◽

Regional Climate ◽

Snow Accumulation ◽

Added Value ◽

Snow Cover Extent ◽

Snow Water

Abstract. This study analyzes the added value of a regional climate model hindcast of CCLM compared to global reanalyses in providing a reconstruction of recent past snow water equivalent (SWE) for Siberia. Consistent regional climate data in time and space is necessary due to lack of station data in that region. We focus on SWE since it represents an important snow cover parameter in a region where snow has the potential to feed back to the climate of the whole Northern Hemisphere. The simulation was performed in a 50 km grid spacing for the period 1948 to 2010 using NCEP Reanalysis 1 as boundary forcing. Daily observational reference data for the period of 1987–2010 was obtained by the satellite derived SWE product of ESA DUE GlobSnow that enables a large scale assessment. The analyses includes comparisons of the distribution of snow cover extent, example time series of monthly SWE for January and April, regional characteristics of long-term monthly mean, standard deviation and temporal correlation averaged over subregions. SWE of CCLM is compared against the SWE information of NCEP-R1 itself and three more reanalyses (NCEP-R2, NCEP-CFSR, ERA-Interim). We demonstrate a significant added value of the CCLM hindcast during snow accumulation period shown for January for many subregions compared to SWE of NCEP-R1. NCEP-R1 mostly underestimates SWE during whole snow season. CCLM overestimates SWE compared to the satellite-derived product during April – a month representing the beginning of snow melt in southern regions. We illustrate that SWE of the regional hindcast is more consistent in time than ERA-Interim and NCEP-R2 and thus add realistic detail.

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