scholarly journals Effect of uncertainties of Southern Ocean surface temperature and sea-ice change on Antarctic climate projections

2018 ◽  
Author(s):  
Julien Beaumet ◽  
Michel Déqué ◽  
Gerhard Krinner ◽  
Cécile Agosta ◽  
Antoinette Alias
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
General Circulation ◽  
Climate Model ◽  
Lower Boundary ◽  
Sea Surface ◽  
Historical Period ◽  
Climate Projections ◽  
Additional Increase ◽  
The Impact

Abstract. In this study, the atmospheric model ARPEGE is used with a stretched grid in order to reach a average horizontal resolution of 35 kilometers over Antarctica. Over the historical period (1981–2010), ARPEGE is forced by the historical sea surface conditions (SSC, i.e. sea surface temperature and sea-ice concentration) from MIROC and NorESM1-M CMIP5 historical runs and by observed SSC (AMIP-experiment). These three simulations are evaluated against ERA-Interim for atmospheric general circulation and against MAR regional climate model and in-situ observations for surface climate. As lower boundary conditions for simulations for the period 2071–2100, we use SSC from coupled climate model CMIP5 simulations of the same models following the RCP8.5 emission scenario. We use these output both directly and with an anomaly method based on quantile mapping. We assess the uncertainties linked to the choice of the coupled model and the impact of the method (direct output and anomalies). For the simulation using SSC from NorESM1-M, we do not find significant changes in climate change signals over Antarctica when using bias-corrected SSC. For the simulation using MIROC-ESM output, an additional increase of +185 Gt yr−1 in precipitation and of +0.8 K in winter temperatures for the grounded Antarctic ice-sheet was obtained when using bias-corrected SSC.

scholarly journals Estimating the Continental Response to Global Warming Using Pattern-Scaled Sea Surface Temperatures and Sea Ice

2016 ◽  
Vol 29 (24) ◽  
pp. 9125-9139 ◽  
Author(s):  
Adeline Bichet ◽  
Paul J. Kushner ◽  
Lawrence Mudryk
Keyword(s):  
Global Warming ◽  
Sea Ice ◽  
General Circulation ◽  
Circulation Model ◽  
Tropical Indian Ocean ◽  
Sea Surface ◽  
Climate Projections ◽  
Western Boundary ◽  
Sea Ice Concentration ◽  
Continental Climate

Abstract Better constraining the continental climate response to anthropogenic forcing is essential to improve climate projections. In this study, pattern scaling is used to extract, from observations, the patterned response of sea surface temperature (SST) and sea ice concentration (SICE) to anthropogenically dominated long-term global warming. The SST response pattern includes a warming of the tropical Indian Ocean, the high northern latitudes, and the western boundary currents. The SICE pattern shows seasonal variations of the main locations of sea ice loss. These SST–SICE response patterns are used to drive an ensemble of an atmospheric general circulation model, the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 5 (CAM5), over the period 1980–2010 along with a standard AMIP ensemble using observed SST—SICE. The simulations enable attribution of a variety of observed trends of continental climate to global warming. On the one hand, the warming trends observed in all seasons across the entire Northern Hemisphere extratropics result from global warming, as does the snow loss observed over the northern midlatitudes and northwestern Eurasia. On the other hand, 1980–2010 precipitation trends observed in winter over North America and in summer over Africa result from the recent decreasing phase of the Pacific decadal oscillation and the recent increasing phase of the Atlantic multidecadal oscillation, respectively, which are not part of the global warming signal. The method holds promise for near-term decadal climate prediction but as currently framed cannot distinguish regional signals associated with oceanic internal variability from aerosol forcing and other sources of short-term forcing.

scholarly journals Assessing bias corrections of oceanic surface conditions for atmospheric models

2019 ◽  
Vol 12 (1) ◽  
pp. 321-342 ◽  
Author(s):  
Julien Beaumet ◽  
Gerhard Krinner ◽  
Michel Déqué ◽  
Rein Haarsma ◽  
Laurent Li
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
General Circulation Models ◽  
Substantial Improvement ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Analogue Method

Abstract. Future sea surface temperature and sea-ice concentration from coupled ocean–atmosphere general circulation models such as those from the CMIP5 experiment are often used as boundary forcings for the downscaling of future climate experiments. Yet, these models show some considerable biases when compared to the observations over present climate. In this paper, existing methods such as an absolute anomaly method and a quantile–quantile method for sea surface temperature (SST) as well as a look-up table and a relative anomaly method for sea-ice concentration (SIC) are presented. For SIC, we also propose a new analogue method. Each method is objectively evaluated with a perfect model test using CMIP5 model experiments and some real-case applications using observations. We find that with respect to other previously existing methods, the analogue method is a substantial improvement for the bias correction of future SIC. Consistency between the constructed SST and SIC fields is an important constraint to consider, as is consistency between the prescribed sea-ice concentration and thickness; we show that the latter can be ensured by using a simple parameterisation of sea-ice thickness as a function of instantaneous and annual minimum SIC.

scholarly journals The impact of climate model sea surface temperature biases on tropical cyclone simulations

2018 ◽  
Vol 53 (1-2) ◽  
pp. 173-192 ◽  
Author(s):  
Wei-Ching Hsu ◽  
Christina M. Patricola ◽  
Ping Chang
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Model ◽  
Sea Surface ◽  
The Impact

scholarly journals Effect of prescribed sea surface conditions on the modern and future Antarctic surface climate simulated by the ARPEGE atmosphere general circulation model

2019 ◽  
Vol 13 (11) ◽  
pp. 3023-3043
Author(s):  
Julien Beaumet ◽  
Michel Déqué ◽  
Gerhard Krinner ◽  
Cécile Agosta ◽  
Antoinette Alias
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Atmospheric Model ◽  
Sea Surface ◽  
Coupled Climate Model ◽  
Surface Climate ◽  
Late 21St Century ◽  
The Antarctic

Abstract. Owing to increase in snowfall, the Antarctic Ice Sheet surface mass balance is expected to increase by the end of the current century. Assuming no associated response of ice dynamics, this will be a negative contribution to sea-level rise. However, the assessment of these changes using dynamical downscaling of coupled climate model projections still bears considerable uncertainties due to poorly represented high-southern-latitude atmospheric circulation and sea surface conditions (SSCs), that is sea surface temperature and sea ice concentration. This study evaluates the Antarctic surface climate simulated using a global high-resolution atmospheric model and assesses the effects on the simulated Antarctic surface climate of two different SSC data sets obtained from two coupled climate model projections. The two coupled models from which SSCs are taken, MIROC-ESM and NorESM1-M, simulate future Antarctic sea ice trends at the opposite ends of the CMIP5 RCP8.5 projection range. The atmospheric model ARPEGE is used with a stretched grid configuration in order to achieve an average horizontal resolution of 35 km over Antarctica. Over the 1981–2010 period, ARPEGE is driven by the SSCs from MIROC-ESM, NorESM1-M and CMIP5 historical runs and by observed SSCs. These three simulations are evaluated against the ERA-Interim reanalyses for atmospheric general circulation as well as the MAR regional climate model and in situ observations for surface climate. For the late 21st century, SSCs from the same coupled climate models forced by the RCP8.5 emission scenario are used both directly and bias-corrected with an anomaly method which consists in adding the future climate anomaly from coupled model projections to the observed SSCs with taking into account the quantile distribution of these anomalies. We evaluate the effects of driving the atmospheric model by the bias-corrected instead of the original SSCs. For the simulation using SSCs from NorESM1-M, no significantly different climate change signals over Antarctica as a whole are found when bias-corrected SSCs are used. For the simulation driven by MIROC-ESM SSCs, a significant additional increase in precipitation and in winter temperatures for the Antarctic Ice Sheet is obtained when using bias-corrected SSCs. For the range of Antarctic warming found (+3 to +4 K), we confirm that snowfall increase will largely outweigh increases in melt and rainfall. Using the end members of sea ice trends from the CMIP5 RCP8.5 projections, the difference in warming obtained (∼ 1 K) is much smaller than the spread of the CMIP5 Antarctic warming projections. This confirms that the errors in representing the Southern Hemisphere atmospheric circulation in climate models are also determinant for the diversity of their projected late 21st century Antarctic climate change.

scholarly journals Assimilating high-resolution sea surface temperature data improves the ocean forecast potential in the Baltic Sea

Ocean Science ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 525-541 ◽  
Author(s):  
Ye Liu ◽  
Weiwei Fu
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Baltic Sea ◽  
Sea Surface ◽  
The Baltic Sea ◽  
Dependent Observations ◽  
The Baltic ◽  
The Impact

Abstract. We assess the impact of assimilating the satellite sea surface temperature (SST) data on the Baltic forecast, particularly on the forecast of ocean variables related to SST. For this purpose, a multivariable data assimilation (DA) system has been developed based on a Nordic version of the Nucleus for European Modelling of the Ocean (NEMO-Nordic). We use Kalman-type filtering to assimilate the observations in the coastal regions. Further, a low-rank approximation of the stationary background error covariance metrics is used at the analysis steps. High-resolution SST from the Ocean and Sea Ice Satellite Application Facility (OSISAF) is assimilated to verify the performance of the DA system. The assimilation run shows very stable improvements of the model simulation as compared with both independent and dependent observations. The SST prediction of NEMO-Nordic is significantly enhanced by the DA forecast. Temperatures are also closer to observations in the DA forecast than the model results in the water above 100 m in the Baltic Sea. In the deeper layers, salinity is also slightly improved. In addition, we find that sea level anomaly (SLA) is improved with the SST assimilation. Comparisons with independent tide gauge data show that the overall root mean square error (RMSE) is reduced by 1.8 % and the overall correlation coefficient is slightly increased. Moreover, the sea-ice concentration forecast is improved considerably in the Baltic Proper, the Gulf of Finland and the Bothnian Sea during the sea-ice formation period, respectively.

scholarly journals A large set of potential past, present and future hydro-meteorological time series for the UK

2018 ◽  
Vol 22 (1) ◽  
pp. 611-634 ◽  
Author(s):  
Benoit P. Guillod ◽  
Richard G. Jones ◽  
Simon J. Dadson ◽  
Gemma Coxon ◽  
Gianbattista Bussi ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Model ◽  
Sea Surface ◽  
Large Set ◽  
Future Scenarios ◽  
High Precipitation ◽  
The Uk

Abstract. Hydro-meteorological extremes such as drought and heavy precipitation can have large impacts on society and the economy. With potentially increasing risks associated with such events due to climate change, properly assessing the associated impacts and uncertainties is critical for adequate adaptation. However, the application of risk-based approaches often requires large sets of extreme events, which are not commonly available. Here, we present such a large set of hydro-meteorological time series for recent past and future conditions for the United Kingdom based on weather@home 2, a modelling framework consisting of a global climate model (GCM) driven by observed or projected sea surface temperature (SST) and sea ice which is downscaled to 25 km over the European domain by a regional climate model (RCM). Sets of 100 time series are generated for each of (i) a historical baseline (1900–2006), (ii) five near-future scenarios (2020–2049) and (iii) five far-future scenarios (2070–2099). The five scenarios in each future time slice all follow the Representative Concentration Pathway 8.5 (RCP8.5) and sample the range of sea surface temperature and sea ice changes from CMIP5 (Coupled Model Intercomparison Project Phase 5) models. Validation of the historical baseline highlights good performance for temperature and potential evaporation, but substantial seasonal biases in mean precipitation, which are corrected using a linear approach. For extremes in low precipitation over a long accumulation period (>3 months) and shorter-duration high precipitation (1–30 days), the time series generally represents past statistics well. Future projections show small precipitation increases in winter but large decreases in summer on average, leading to an overall drying, consistently with the most recent UK Climate Projections (UKCP09) but larger in magnitude than the latter. Both drought and high-precipitation events are projected to increase in frequency and intensity in most regions, highlighting the need for appropriate adaptation measures. Overall, the presented dataset is a useful tool for assessing the risk associated with drought and more generally with hydro-meteorological extremes in the UK.

Robust Late 21st Century Shift in the Regional Monsoons in RegCM-CORDEX Simulations

2020 ◽  
Author(s):  
Moetasim Ashfaq ◽  
Tereza Cavazos ◽  
Michelle Reboita ◽  
José Abraham Torres-Alavez ◽  
Eun-Soon Im ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
General Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
Lower Boundary ◽  
Coupled Model ◽  
General Circulation Models ◽  
Monsoon Onset ◽  
Historical Period ◽  
Monsoon Period

<p>We use an unprecedented ensemble of regional climate model (RCM) projections over seven regional CORDEX domains to provide, for the first time, an RCM-based global view of monsoon changes at various levels of increased greenhouse gas (GHG) forcing. All regional simulations are conducted using RegCM4 at a 25km horizontal grid spacing using lateral and lower boundary forcing from three General Circulation Models (GCMs), which are part of the fifth phase of the Coupled Model Inter-comparison Project (CMIP5). Each simulation covers the period from 1970 through 2100 under two Representative Concentration Pathways (RCP2.6 and RCP8.5). Regional climate simulations exhibit high fidelity in capturing key characteristics of precipitation and atmospheric dynamics across monsoon regions in the historical period. In the future period, regional monsoons exhibit a spatially robust delay in the monsoon onset, an increase in seasonality, and a reduction in the rainy season length at higher levels of radiative forcing. All regions with substantial delays in the monsoon onset exhibit a decrease in pre-monsoon precipitation, indicating a strong connection between pre-monsoon drying and a shift in the monsoon onset. The weakening of latent heat driven atmospheric warming during the pre-monsoon period delays the overturning of atmospheric subsidence in the monsoon regions, which defers their transitioning into deep convective states. Monsoon changes under the RCP2.6 scenario are mostly within the baseline variability. </p>

scholarly journals Using an AGCM to Diagnose Historical Effective Radiative Forcing and Mechanisms of Recent Decadal Climate Change

2014 ◽  
Vol 27 (3) ◽  
pp. 1193-1209 ◽  
Author(s):  
Timothy Andrews
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Radiative Forcing ◽  
General Circulation ◽  
Circulation Model ◽  
Sea Surface ◽  
Aerosol Forcing ◽  
Effective Radiative Forcing

Abstract An atmospheric general circulation model is forced with observed monthly sea surface temperature and sea ice boundary conditions, as well as forcing agents that vary in time, for the period 1979–2008. The simulations are then repeated with various forcing agents, individually and in combination, fixed at preindustrial levels. The simple experimental design allows the diagnosis of the model’s global and regional time-varying effective radiative forcing from 1979 to 2008 relative to preindustrial levels. Furthermore the design can be used to (i) calculate the atmospheric model’s feedback/sensitivity parameters to observed changes in sea surface temperature and (ii) separate those aspects of climate change that are directly driven by the forcing from those driven by large-scale changes in sea surface temperature. It is shown that the atmospheric response to increased radiative forcing over the last 3 decades has halved the global precipitation response to surface warming. Trends in sea surface temperature and sea ice are found to contribute only ~60% of the global land, Northern Hemisphere, and summer land warming trends. Global effective radiative forcing is ~1.5 W m−2 in this model, with anthropogenic and natural contributions of ~1.3 and ~0.2 W m−2, respectively. Forcing increases by ~0.5 W m−2 decade−1 over the period 1979–2008 or ~0.4 W m−2 decade−1 if years strongly influenced by volcanic forcings—which are nonlinear with time—are excluded from the trend analysis. Aerosol forcing shows little global decadal trend due to offsetting regional trends whereby negative aerosol forcing weakens in Europe and North America but continues to strengthen in Southeast Asia.

High sensitivity of seasonal tropical precipitation to local sea-surface temperature

2020 ◽  
Author(s):  
Robin Chadwick ◽  
Peter Good ◽  
Christopher Holloway ◽  
John Kennedy ◽  
Jason Lowe ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Model ◽  
Sea Surface ◽  
Climate Projections ◽  
Office Work ◽  
Shallow Convection ◽  
Seasonal Forecasts ◽  
Tropical Precipitation ◽  
Observational Uncertainty

<p>Seasonal mean tropical precipitation at any location is controlled by a tangle of local and remote effects, including influences from SSTs across the globe. This, along with uncertainty in precipitation observations, and extremely limited observations of atmospheric circulation, makes understanding the relevant physics challenging. Climate model precipitation biases persisting across multiple generations of models point towards stubborn gaps in understanding and reduce confidence in seasonal forecasts and climate projections.  This includes the 'double ITCZ problem': excessive rainfall in the southern tropical Pacific, first reported in 1995.  Model ITCZs also tend to be too wide.</p><p>Our study shows that in the real world, the sensitivity of tropical precipitation to local sea surface temperature is high, associated with strong shallow circulations.  This rests on a novel analysis of observations, unpicking local and remote controls on precipitation, and navigating a path through observational uncertainty.  Models with appropriate sensitivity to local sea surface temperature, perform well across many conditions.  Improvements in this sensitivity from the fifth to the sixth model intercomparison project are small, highlighting the need for new understanding.  By further linking model biases to shallow convection, our results highlight a target process for focused research: accelerating improvements in seasonal forecasts through to multi-decadal climate projections.</p><p>Wider Met Office work linking precipitation evaluation between climate, seasonal and weather timescales will also be summarised.</p>

Global monthly sea surface temperature and sea ice reconstruction for historical simulations

2020 ◽  
Author(s):  
Eric Samakinwa ◽  
Stefan Brönnimann
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
General Circulation Models ◽  
Sea Surface ◽  
Annual Variability ◽  
Instrumental Records

<p>Variability in Sea Surface Temperature (SST) is one of the prime sources of intra-annual variability, and also an important boundary condition for Atmospheric General Circulation Models (AGCMs). In many AGCM simulations, SST and Sea Ice Concentration (SIC) are prescribed. While SSTs are specified according to observations available in recent period of instrumental records (1850 – present), SIC depends on climatological averages with less variability prior to the inception of satellite measurements. This limits our understanding of large-scale climate variations in the past.</p><p>In this study, we augment multi-proxy reconstructed annual mean temperature of Neukom et al. (2019) with intra-annual variability from HadISST (v2.0), for 850 years (1000 – 1849). Intra-seasonal variability, such as the phase-locking of El-Nino Southern Oscillation, Indian Ocean Dipole and Tropical Atlantic SST indices to annual-cycle, are utilized. The intra-annual component of HadISST and SST indices estimated from the multi-proxy reconstructed annual mean, are used to develop grid-based multivariate linear regression models using the Frisch-Waugh-Lovell theorem, in a monthly stratified approach. Furthermore, we introduce a scaling technique to ensure homogeneous mean and variance, similar to that of the target. SST observations obtained from ship measurements by ICOADS before 1850, will be integrated in an off-line data assimilation approach.</p><p>Similarly, we reconstruct SIC via analogue resampling of HadISST SIC (1941 – 2000), for both hemispheres. We pool our analogues in four seasons, comprising of 3 months each, such that for each month within a season, there are 180 possible analogues. The best analogues are selected based on correlation coefficients between reconstructed SST and its target.</p>

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