scholarly journals The Influence of Changes in Cloud Cover on Recent Surface Temperature Trends in the Arctic

2008 ◽  
Vol 21 (4) ◽  
pp. 705-715 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key ◽  
Xuanji Wang
Keyword(s):  
Surface Temperature ◽  
Cloud Cover ◽  
Temperature Trend ◽  
The Arctic ◽  
Surface Warming ◽  
Weather Forecasts ◽  
Temperature Trends ◽  
Four Seasons ◽  
Sky Conditions ◽  
Arctic Surface

Abstract A method is presented to assess the influence of changes in Arctic cloud cover on the surface temperature trend, allowing for a more robust diagnosis of causes for surface warming or cooling. Seasonal trends in satellite-derived Arctic surface temperature under clear-, cloudy-, and all-sky conditions are examined for the period 1982–2004. The satellite-derived trends are in good agreement with trends in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product and surface-based weather station measurements in the Arctic. Surface temperature trends under clear and cloudy conditions have patterns similar to the all-sky trends, though the magnitude of the trends under cloudy conditions is smaller than those under clear-sky conditions, illustrating the negative feedback of clouds on the surface temperature trends. The all-sky surface temperature trend is divided into two parts: the first part is a linear combination of the surface temperature trends under clear and cloudy conditions; the second part is caused by changes in cloud cover as a function of the clear–cloudy surface temperature difference. The relative importance of these two components is different in the four seasons, with the first part more important in spring, summer, and autumn, but with both parts being equally important in winter. The contribution of biases in satellite retrievals is also evaluated.

scholarly journals Pacific contribution to decadal surface temperature trends in the Arctic during the twentieth century

2021 ◽  
Author(s):  
Lea Svendsen ◽  
Noel Keenlyside ◽  
Morven Muilwijk ◽  
Ingo Bethke ◽  
Nour-Eddine Omrani ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Surface Temperature ◽  
Decadal Variability ◽  
The Arctic ◽  
External Forcing ◽  
Arctic Warming ◽  
Temperature Trends ◽  
The Pacific ◽  
Instrumental Records ◽  
Arctic Surface

AbstractInstrumental records suggest multidecadal variability in Arctic surface temperature throughout the twentieth century. This variability is caused by a combination of external forcing and internal variability, but their relative importance remains unclear. Since the early twentieth century Arctic warming has been linked to decadal variability in the Pacific, we hypothesize that the Pacific could impact decadal temperature trends in the Arctic throughout the twentieth century. To investigate this, we compare two ensembles of historical all-forcing twentieth century simulations with the Norwegian Earth System Model (NorESM): (1) a fully coupled ensemble and (2) an ensemble where momentum flux anomalies from reanalysis are prescribed over the Indo-Pacific Ocean to constrain Pacific sea surface temperature variability. We find that the combined effect of tropical and extratropical Pacific decadal variability can explain up to ~ 50% of the observed decadal surface temperature trends in the Arctic. The Pacific-Arctic connection involves both lower tropospheric horizontal advection and subsidence-induced adiabatic heating, mediated by Aleutian Low variations. This link is detected across the twentieth century, but the response in Arctic surface temperature is moderated by external forcing and surface feedbacks. Our results also indicate that increased ocean heat transport from the Atlantic to the Arctic could have compensated for the impact of a cooling Pacific at the turn of the twenty-first century. These results have implications for understanding the present Arctic warming and future climate variations.

scholarly journals Intercomparison of temperature trends in IPCC CMIP5 simulations with observations, reanalyses and CMIP3 models

2013 ◽  
Vol 6 (5) ◽  
pp. 1705-1714 ◽  
Author(s):  
J. Xu ◽  
L. Zhao ◽  
Keyword(s):  
Coupled Model ◽  
Temperature Trend ◽  
The Arctic ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Model Simulations ◽  
Temperature Trends ◽  
Intercomparison Project ◽  
Stratospheric Cooling ◽  
The Tropics

Abstract. On the basis of the fifth Coupled Model Intercomparison Project (CMIP5) and the climate model simulations covering 1979 through 2005, the temperature trends and their uncertainties have been examined to note the similarities or differences compared to the radiosonde observations, reanalyses and the third Coupled Model Intercomparison Project (CMIP3) simulations. The results show noticeable discrepancies for the estimated temperature trends in the four data groups (radiosonde, reanalysis, CMIP3 and CMIP5), although similarities can be observed. Compared to the CMIP3 model simulations, the simulations in some of the CMIP5 models were improved. The CMIP5 models displayed a negative temperature trend in the stratosphere closer to the strong negative trend seen in the observations. However, the positive tropospheric trend in the tropics is overestimated by the CMIP5 models relative to CMIP3 models. While some of the models produce temperature trend patterns more highly correlated with the observed patterns in CMIP5, the other models (such as CCSM4 and IPSL_CM5A-LR) exhibit the reverse tendency. The CMIP5 temperature trend uncertainty was significantly reduced in most areas, especially in the Arctic and Antarctic stratosphere, compared to the CMIP3 simulations. Similar to the CMIP3, the CMIP5 simulations overestimated the tropospheric warming in the tropics and Southern Hemisphere and underestimated the stratospheric cooling. The crossover point where tropospheric warming changes into stratospheric cooling occurred near 100 hPa in the tropics, which is higher than in the radiosonde and reanalysis data. The result is likely related to the overestimation of convective activity over the tropical areas in both the CMIP3 and CMIP5 models. Generally, for the temperature trend estimates associated with the numerical models including the reanalyses and global climate models, the uncertainty in the stratosphere is much larger than that in the troposphere, and the uncertainty in the Antarctic is the largest. In addition, note that the reanalyses show the largest uncertainty in the lower tropical stratosphere, and the CMIP3 simulations show the largest uncertainty in both the south and north polar regions.

scholarly journals Comparison and integration of ice-pack temperatures derived from AVHRR and passive microwave imagery

1993 ◽  
Vol 17 ◽  
pp. 372-378 ◽  
Author(s):  
J. Maslanik ◽  
J. Key
Keyword(s):  
Cloud Cover ◽  
Large Scale ◽  
Passive Microwave ◽  
The Arctic ◽  
First Year ◽  
Brightness Temperatures ◽  
Ice Surface ◽  
Ice Concentration ◽  
Avhrr Data ◽  
Sky Conditions

Co-located sets of AVHRR and SSM/I passive microwave imagery are used to estimate ice surface temperatures and to infer cloud cover in the Arctic. Physical temperatures are determined from the AVHRR data by modeling atmospheric and surface conditions. The resulting field-of-view temperatures are converted to ice surface skin temperatures by adjusting for ice concentration calculated using the SSM/I data. By selecting AVHRR-derived temperatures for clear sky conditions, “effective” emissivities of first-year and multi-year ice are calculated. Given these emissivities, microwave brightness temperatures, and proportions of first-year and multi-year ice as estimated using the NASA Team Algorithm, physical temperatures of the sea ice/snow surface are calculated that are, in theory, relatively independent of cloud conditions. The resulting ice temperatures are used to delineate a portion of the cloud cover in the AVHRR data. The advantages of this approach are that only a fairly small amount of AVHRR data are needed to calibrate the SSM/I imagery that can then be used to calculate a time-series of temperatures on a large scale.

scholarly journals Comparison of satellite, thermochron and air temperatures at Summit, Greenland, during the winter of 2008/09

2010 ◽  
Vol 56 (198) ◽  
pp. 735-741 ◽  
Author(s):  
Lora S. Koenig ◽  
Dorothy K. Hall
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Satellite Data ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Seasonal Temperature ◽  
Temperature Trends ◽  
Air Temperatures ◽  
Winter Time ◽  
Arctic Surface

AbstractCurrent trends show a rise in Arctic surface and air temperatures, including over the Greenland ice sheet where rising temperatures will contribute to increased sea-level rise through increased melt. We aim to establish the uncertainties in using satellite-derived surface temperature for measuring Arctic surface temperature, as satellite data are increasingly being used to assess temperature trends. To accomplish this, satellite-derived surface temperature, or land-surface temperature (LST), must be validated and limitations of the satellite data must be assessed quantitatively. During the 2008/09 boreal winter at Summit, Greenland, we employed data from standard US National Oceanic and Atmospheric Administration (NOAA) air-temperature instruments, button-sized temperature sensors called thermochrons and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument to (1) assess the accuracy and utility of thermochrons in an ice-sheet environment and (2) compare MODIS-derived LSTs with thermochron-derived surface and air temperatures. The thermochron-derived air temperatures were very accurate, within 0.1 ± 0.3°C of the NOAA-derived air temperature, but thermochron-derived surface temperatures were ∼3°C higher than MODIS-derived LSTs. Though surface temperature is largely determined by air temperature, these variables can differ significantly. Furthermore, we show that the winter-time mean air temperature, adjusted to surface temperature, was ∼11°C higher than the winter-time mean MODIS-derived LST. This marked difference occurs largely because satellite-derived LSTs cannot be measured through cloud cover, so caution must be exercised in using time series of satellite LST data to study seasonal temperature trends.

scholarly journals Arctic Climate Variability and Trends from Satellite Observations

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Xuanji Wang ◽  
Jeffrey Key ◽  
Yinghui Liu ◽  
Charles Fowler ◽  
James Maslanik ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Surface Temperature ◽  
Cloud Fraction ◽  
Arctic Sea Ice ◽  
Satellite Observations ◽  
Arctic Climate ◽  
The Arctic ◽  
Climate Indices ◽  
Surface Warming

Arctic climate has been changing rapidly since the 1980s. This work shows distinctly different patterns of change in winter, spring, and summer for cloud fraction and surface temperature. Satellite observations over 1982–2004 have shown that the Arctic has warmed up and become cloudier in spring and summer, but cooled down and become less cloudy in winter. The annual mean surface temperature has increased at a rate of 0.34°C per decade. The decadal rates of cloud fraction trends are −3.4%, 2.3%, and 0.5% in winter, spring, and summer, respectively. Correspondingly, annually averaged surface albedo has decreased at a decadal rate of −3.2%. On the annual average, the trend of cloud forcing at the surface is −2.11 W/m2per decade, indicating a damping effect on the surface warming by clouds. The decreasing sea ice albedo and surface warming tend to modulate cloud radiative cooling effect in spring and summer. Arctic sea ice has also declined substantially with decadal rates of −8%, −5%, and −15% in sea ice extent, thickness, and volume, respectively. Significant correlations between surface temperature anomalies and climate indices, especially the Arctic Oscillation (AO) index, exist over some areas, implying linkages between global climate change and Arctic climate change.

scholarly journals Intercomparison of temperature trends in IPCC CMIP5 simulations with observations, reanalyses and CMIP3 models

2012 ◽  
Vol 5 (4) ◽  
pp. 3621-3645 ◽  
Author(s):  
J. Xu ◽  
A. M. Powell
Keyword(s):  
Coupled Model ◽  
Temperature Trend ◽  
The Arctic ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Model Simulations ◽  
Temperature Trends ◽  
Intercomparison Project ◽  
Stratospheric Cooling ◽  
The Tropics

Abstract. On the basis of the fifth Coupled Model Intercomparison Project (CMIP5) and the climate model simulations covering 1979 through 2005, the temperature trends and their uncertainties have been examined to note the similarities or differences compared to the radiosonde observations, reanalyses and the third Coupled Model Intercomparison Project (CMIP3) simulations. The results show noticeable discrepancies for the estimated temperature trends in the four data groups (Radiosonde, Reanalysis, CMIP3 and CMIP5) although similarities can be observed. Compared to the CMIP3 model simulations, the simulation in some of CMIP5 models were improved. The CMIP5 models displayed a negative temperature trend in the stratosphere closer to the strong negative trend seen in the observations. However, the positive tropospheric trend in the tropics is overestimated by the CMIP5 models relative to CMIP3 models. While some of the models produce temperature trend patterns more highly correlated with the observed patterns in CMIP5, the other models (such as CCSM4 and IPSL_CM5A-LR) exhibit the reverse tendency. The CMIP5 temperature trend uncertainty was significantly reduced in most areas, especially in the Arctic and Antarctic stratosphere, compared to the CMIP3 simulations. Similar to the CMIP3, the CMIP5 simulations overestimated the tropospheric warming in the tropics and Southern Hemisphere and underestimated the stratospheric cooling. The crossover point where tropospheric warming changes into stratospheric cooling occurred near 100 hPa in the tropics, which is higher than in the radiosonde and reanalysis data. The result is likely related to the overestimation of convective activity over the tropical areas in both the CMIP3 and CMIP5 models. Generally, for the temperature trend estimates associated with the numerical models including the reanalyses and global climate models, the uncertainty in the stratosphere is much larger than that in the troposphere, and the uncertainty in the Antarctic is the largest. In addition, note that the reanalyses show the largest uncertainty in the lower tropical stratosphere, and the CMIP3 simulations show the largest uncertainty in both the south and north polar regions.

scholarly journals Evaluation of 2-m Air Temperature and Surface Temperature from ERA5 and ERA-I Using Buoy Observations in the Arctic during 2010–2020

2021 ◽  
Vol 13 (14) ◽  
pp. 2813
Author(s):  
Yining Yu ◽  
Wanxin Xiao ◽  
Zhilun Zhang ◽  
Xiao Cheng ◽  
Fengming Hui ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Weather Forecasts ◽  
Ice Concentration ◽  
Medium Range ◽  
In Situ Observations ◽  
Two Temperature

In data-sparse regions such as the Arctic, atmospheric reanalysis is one of the key tools for understanding rapid climate change at the regional and global scales. The utility of reanalysis datasets based on data assimilation is affected by their accuracy and biases. Therefore, it is important to evaluate their performance. Here, we conduct inter-comparisons of two temperature variables, namely, the 2-m air temperature (Ta) and the surface temperature (Ts), from the widely used ERA-I and ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) against in situ observations from three international buoy programs (i.e., the International Arctic Buoy Programme (IABP), the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), and the Cold Regions Research and Engineering Laboratory (CRREL)) during 2010–2020 in the Arctic. Overall, the results show that both the ERA-I and ERA5 were well correlated with the buoy observations, with the highest correlation coefficient reaching 0.98. There were generally warm Ta biases for both ERA-I (2.27 ± 3.33 °C) and ERA5 (2.34 ± 3.22 °C) when compared with more than 3000 matching pairs of daily buoy observations. The warm Ta biases of both reanalysis datasets exhibited seasonal variations, reaching the maximum of 3.73 ± 2.84 °C in April and the minimum of 1.36 ± 2.51 °C in September. For Ts, both ERA-I and ERA5 exhibited good consistencies with the buoy observations, but have higher amplitude biases compared with those for Ta, with generally negative biases of −4.79 ± 4.86 °C for ERA-I and −4.11 ± 3.92 °C for ERA5. For both reanalysis datasets, the largest bias of Ts (−11.18 ± 3.08 °C) occurred in December, while the biases were rather small (less than −3 °C) in the warmer months (April to October). The cold Ts biases for ERA-I and ERA5 were probably overestimated due to the location of the surface temperature sensors on the buoys, which may have been affected by snow cover. Both the Ta and Ts biases varied for different buoy programs and different sea ice concentration conditions, yet they exhibited similar trends.

scholarly journals Isolating the Temperature Feedback Loop and Its Effects on Surface Temperature

2016 ◽  
Vol 73 (8) ◽  
pp. 3287-3303 ◽  
Author(s):  
Sergio A. Sejas ◽  
Ming Cai
Keyword(s):  
Surface Temperature ◽  
Feedback Loop ◽  
Temperature Response ◽  
Feedback Loops ◽  
The Arctic ◽  
Response Analysis ◽  
Surface Warming ◽  
Temperature Feedback ◽  
The Tropics ◽  
Temperature Responses

Abstract Climate feedback processes are known to substantially amplify the surface warming response to an increase of greenhouse gases. When the forcing and feedbacks modify the temperature response they trigger temperature feedback loops that amplify the direct temperature changes due to the forcing and nontemperature feedbacks through the thermal–radiative coupling between the atmosphere and surface. This study introduces a new feedback-response analysis method that can isolate and quantify the effects of the temperature feedback loops of individual processes on surface temperature from their corresponding direct surface temperature responses. The authors analyze a 1% yr−1 increase of CO2 simulation of the NCAR CCSM4 at the time of CO2 doubling to illustrate the new method. The Planck sensitivity parameter, which indicates colder regions experience stronger surface temperature responses given the same change in surface energy flux, is the inherent factor that leads to polar warming amplification (PWA). This effect explains the PWA in the Antarctic, while the direct temperature response to the albedo and cloud feedbacks further explains the greater PWA of the Arctic. Temperature feedback loops, particularly the one associated with the albedo feedback, further amplify the Arctic surface warming relative to the tropics. In the tropics, temperature feedback loops associated with the CO2 forcing and water vapor feedback cause most of the surface warming. Overall, the temperature feedback is responsible for most of the surface warming globally, accounting for nearly 76% of the global-mean surface warming. This is 3 times larger than the next largest warming contribution, indicating that the temperature feedback loop is the preeminent contributor to the surface warming.

A Study on the Change of Arctic Ocean Surface Temperature in CESM

2020 ◽  
Author(s):  
Peiyan Xie ◽  
Hailun He ◽  
Shuang Li
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Global Climate ◽  
The Arctic ◽  
Lower Latitude ◽  
Surface Warming ◽  
Arctic Sea ◽  
Community Earth System Model ◽  
The Arctic Oscillation ◽  
Arctic Area

<p>Since the 1950s, human has begun to explore the Arctic area. As the scientific research goes further, scientists gradually realize the important role the Arctic plays in the global climate system, and it has been said the Arctic has an amplifying effect on surface warming, which increases 2 to 3 times faster than the global average increment. Given the importance of this area, we try to figure out the relationship among the Arctic sea surface temperature (SST), sea ice index and the Arctic Oscillation (AO) in this paper. By using Community Earth System Model (CESM), we calculated an ocean-seaice-atmosphere coupled 200-year experiment. As a result, we found out that the variation of Arctic SST is negatively correlated with the change of sea ice area. There is a significant correlation between the change of SST and AO, which can lead to the anomaly of air heat transport between the Arctic area and the areas in lower latitude.</p>

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