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The Arctic region is responding heavily to climate change, and yet, the air temperature of ice-covered areas in the Arctic is heavily under-sampled when it comes to in situ measurements, resulting in large uncertainties in existing weather and reanalysis products. This paper presents a method for estimating daily mean clear-sky 2 m air temperatures (T2m) in the Arctic from satellite observations of skin temperature, using the Arctic and Antarctic ice Surface Temperatures from thermal Infrared (AASTI) satellite dataset, providing spatially detailed observations of the Arctic. The method is based on a linear regression model, which has been tuned against in situ observations to estimate daily mean T2m based on clear-sky satellite ice surface skin temperatures. The daily satellite-derived T2m product includes estimated uncertainties and covers the Arctic sea ice and the Greenland Ice Sheet during clear skies for the period 2000–2009, provided on a 0.25 ∘ regular latitude–longitude grid. Comparisons with independent in situ measured T2m show average biases of 0.30 and 0.35 ∘ C and average root-mean-square errors of 3.47 and 3.20 ∘C for land ice and sea ice, respectively. The associated uncertainties are verified to be very realistic for both land ice and sea ice, using in situ observations. The reconstruction provides a much better spatial coverage than the sparse in situ observations of T2m in the Arctic and is independent of numerical weather prediction model input. Therefore, it provides an important supplement to simulated air temperatures to be used for assimilation or global surface temperature reconstructions. A comparison of T2m derived from satellite and ERA-Interim/ERA5 estimates shows that the satellite-derived T2m validates similar to or better than ERA-Interim/ERA5 against in situ measurements in the Arctic. [ABSTRACT FROM AUTHOR]

The frequent and persistent cloud cover in the Arctic limits the extent to which sea surface temperature (SST) can be retrieved from thermal infrared (IR) satellite sensors. Passive microwave (PMW) observations provide highly complementary information to IR, enabling measurements through non-precipitating clouds, although at a coarser spatial resolution. The differences in coverage, accuracy, footprint size, spatial resolution and error characteristics between IR and PMW SSTs require a systematic assessment of how to best combine IR and PMW SST retrievals. This is provided in this study on the basis of the ESA-CCI PMW SST climate data record (CDR) and an existing IR-based gap-free SST and sea ice surface temperature CDR covering the Arctic (> 58 ° N), where cloud cover is a serious limitation to IR sensors. An important step towards a combined IR and PMW SST CDR is to correct for systematic biases in the PMW and IR SST datasets relative to each other. The PMW SSTs show reduced biases against in situ SSTs compared to the IR SSTs, but for consistency with time periods when no Arctic PMW SSTs were available, the PMW SSTs have been adjusted to the IR SSTs in this study. This is done using a dynamic bias correction to generate a consistent combined IR and PMW Arctic SST CDR for the period 2002–2017. Including PMW SSTs reduces the standard deviations from 0.54 °C, 0.55 °C and 0.47 °C to 0.47 °C, 0.54 °C and 0.41 °C against drifters, moorings and Argo floats, respectively. The improved performance is seen in almost all regions (including those already covered by IR observations), with the largest improvement in IR data sparse regions. The average theoretical uncertainty reduces by 0.08 °C, which is in good agreement with the observed improvement in the standard deviation against drifters. The results are very promising and expected to improve even further in the future with the launch of the Copernicus Imaging Microwave Radiometer (CIMR), which will enable PMW SST retrievals with lower uncertainties and much closer to coasts and sea ice (where the largest uncertainties arise) than what is possible with previous and current PMW radiometers. • Optimal blending of infrared and microwave sea surface temperatures in the Arctic. • Improved spatial coverage when including microwave satellite observations. • Reduced standard deviations against in situ observations in almost all regions. [ABSTRACT FROM AUTHOR]

The Arctic marine environment is undergoing a transition from thick multi-year to first-year sea-ice cover with coincident lengthening of the melt season. Such changes are evident in the Baffin Bay-Davis Strait-Labrador Sea (BDL) region where melt onset has occurred ~8 days decade−1 earlier from 1979 to 2015. A series of anomalously early events has occurred since the mid-1990s, overlapping a period of increased upper-air ridging across Greenland and the northwestern North Atlantic. We investigate an extreme early melt event observed in spring 2013. (~6 σ below the 1981–2010 melt climatology), with respect to preceding sub-seasonal mid-tropospheric circulation conditions as described by a daily Greenland Blocking Index (GBI). The 40-days prior to the 2013 BDL melt onset are characterized by a persistent, strong 500 hPa anticyclone over the region (GBI >+1 on >75% of days). This circulation pattern advected warm air from northeastern Canada and the northwestern Atlantic poleward onto the thin, first-year sea ice and caused melt ~50 days earlier than normal. The episodic increase in the ridging atmospheric pattern near western Greenland as in 2013, exemplified by large positive GBI values, is an important recent process impacting the atmospheric circulation over a North Atlantic cryosphere undergoing accelerated regional climate change. [ABSTRACT FROM AUTHOR]

The surface temperature is one of the main parameters for assessing climate change. Temperature change is most pronounced in the Arctic, and therefore, it is crucial to accurately estimate sea and sea ice surface temperatures in this region. The availability of in situ observations is limited in the Arctic, thus, increasing the need for satellite observations to estimate surface temperatures. We present the first Arctic (>58°N) gap-free climate dataset covering the surface temperatures of the ocean, sea ice and the marginal ice zone from 1 January 1982 to 31 May 2021 based on observations from infrared satellite sensors. The underlying algorithm combines the multi-satellite observations and performs a statistical optimal interpolation to obtain daily gap-free fields, with a spatial resolution of 0.05° in latitude and longitude. In situ observations have been used to derive consistent validation statistics over the ocean and sea ice. Comparison of the derived sea surface temperatures against in situ measurements from drifting buoys, moored buoys and Argo floats shows mean differences of 0.01 °C, 0.04 °C and 0.04 °C and standard deviations of 0.54 °C, 0.56 °C and 0.51 °C, respectively. Over sea ice, the derived ice surface temperatures have been compared with KT-19 measurements from IceBridge flights, showing a mean difference of 1.52 °C and standard deviation of 3.12 °C, and with air temperatures from the North Pole (NP) ice drifting stations as well as ECMWF distributed buoys and CRREL buoys, with mean differences of −2.35 °C, −3.21 °C and –2.87 °C and standard deviations of 3.12 °C, 3.34 °C and 3.36 °C, respectively. The combination of sea and sea-ice surface temperature provides a consistent dataset for climate analysis, which is crucial for studying climate change and trends in the Arctic. The combined sea and sea-ice surface temperature of the Arctic has risen with about 4.5 °C over the period 1982–2021, with a peak warming of around 10 °C in the northeastern Barents Sea. • Gap-free sea and sea-ice surface temperatures of the Arctic (>58°N) from satellites. • Arctic sea and sea-ice surface temperature increase of about 4.5 °C from 1982 to 2021. • A peak warming of about 10 °C is observed in the north-eastern Barents Sea. [ABSTRACT FROM AUTHOR]

Abstract: A multi-sensor bias correction method has been developed using satellite sea surface temperature (SST) products from one microwave and five infrared sensors that cover the Arctic Ocean. The correction method has been used to construct improved single sensor and multi-sensor, merged and interpolated satellite SST products from January to December 2008. The validation of the satellite products ingested in the level 4 production shows that large biases can persist for months in this region. The SST products from the AATSR sensor on ENVISAT and the NAVOCEANO AVHRR GAC are the most stable and reliable products for the Arctic region. These products have therefore been used to construct the reference product against which the other satellite products have been corrected. The bias correction method has been developed using detailed error characteristics and thorough time-space analysis, and the bias corrected fields are validated against in situ observations from drifting buoys. All the individual satellite products show improvement in both bias and standard deviation after correction. Largest improvements are found for the Modis sensor on the Terra satellite, where biases are improved from −0.46K to 0.02K with the correction method. Temporal validation statistics reveal that extended periods with significant biases are also removed by the bias correction method. A significant improvement is seen when the corrected satellite products are used for the SST analysis. When compared against drifting buoys, not included in the analysis, the corrected level 4 satellite SST product shows a bias of −0.04K and standard deviation of 0.54K, compared to an original bias of −0.28K and standard deviation of 0.61K. The effect of a missing reference sensor is assessed for the full period. Level 4 test runs using only one reference sensor demonstrates that improvements can be obtained with a single sensor bias correction method, and that the AATSR sensor gives the largest improvements. However, using both reference sensors in the bias calculation gives significantly better performance than using just one. [Copyright &y& Elsevier]