We examine how the evaluation of modelled sea-ice coverage against reality is affected by uncertainties in the retrieval of sea-ice coverage from satellite, by the usage of sea-ice extent to overcome ...these uncertainties, and by internal variability. We find that for Arctic summer sea ice, model biases in sea-ice extent can be qualitatively different from biases in sea-ice area. This is because about half of the CMIP5 models and satellite retrievals based on the Bootstrap and the ASI algorithm show a compact ice cover in summer with large areas of high-concentration sea ice, while the other half of the CMIP5 models and satellite retrievals based on the NASA Team algorithm show a loose ice cover. For the Arctic winter sea-ice cover, differences in grid geometry can cause synthetic biases in sea-ice extent that are larger than the observational uncertainty. Comparing the uncertainty arising directly from the satellite retrievals with those that arise from internal variability, we find that the latter by far dominates the uncertainty estimate for trends in sea-ice extent and area: most of the differences between modelled and observed trends can simply be explained by internal variability. For absolute sea-ice area and sea-ice extent, however, internal variability cannot explain the difference between model and observations for about half the CMIP5 models that we analyse here. All models that we examined have regional biases, as expressed by the root-mean-square error in concentration, that are larger than the differences between individual satellite algorithms.
The MPI‐ESM1.2 is the latest version of the Max Planck Institute Earth System Model and is the baseline for the Coupled Model Intercomparison Project Phase 6 and current seasonal and decadal climate ...predictions. This paper evaluates a coupled higher‐resolution version (MPI‐ESM1.2‐HR) in comparison with its lower‐resolved version (MPI‐ESM1.2‐LR). We focus on basic oceanic and atmospheric mean states and selected modes of variability, the El Niño/Southern Oscillation and the North Atlantic Oscillation. The increase in atmospheric resolution in MPI‐ESM1.2‐HR reduces the biases of upper‐level zonal wind and atmospheric jet stream position in the northern extratropics. This results in a decrease of the storm track bias over the northern North Atlantic, for both winter and summer season. The blocking frequency over the European region is improved in summer, and North Atlantic Oscillation and related storm track variations improve in winter. Stable Atlantic meridional overturning circulations are found with magnitudes of ~16 Sv for MPI‐ESM1.2‐HR and ~20 Sv for MPI‐ESM1.2‐LR at 26°N. A strong sea surface temperature bias of ~5°C along with a too zonal North Atlantic current is present in both versions. The sea surface temperature bias in the eastern tropical Atlantic is reduced by ~1°C due to higher‐resolved orography in MPI‐ESM‐HR, and the region of the cold‐tongue bias is reduced in the tropical Pacific. MPI‐ESM1.2‐HR has a well‐balanced radiation budget and its climate sensitivity is explicitly tuned to 3 K. Although the obtained reductions in long‐standing biases are modest, the improvements in atmospheric dynamics make this model well suited for prediction and impact studies.
Key Points
A higher‐resolution version of MPI‐ESM1.2 is presented, which has a well‐balanced radiation budget and stable ocean circulation
The higher atmospheric resolution improves North Atlantic storm tracks, blocking frequency, and NAO representation
The higher computational costs remain manageable and enable studies of seasonal to decadal predictions and climate impacts
MPI‐ESM is a new version of the global Earth system model developed at the Max Planck Institute for Meteorology. This paper describes the ocean state and circulation as well as basic aspects of ...variability in simulations contributing to the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The performance of the ocean/sea‐ice model MPIOM, coupled to a new version of the atmosphere model ECHAM6 and modules for land surface and ocean biogeochemistry, is assessed for two model versions with different grid resolution in the ocean. The low‐resolution configuration has a nominal resolution of 1.5°, whereas the higher resolution version features a quasiuniform, eddy‐permitting global resolution of 0.4°. The paper focuses on important oceanic features, such as surface temperature and salinity, water mass distribution, large‐scale circulation, and heat and freshwater transports. In general, these integral quantities are simulated well in comparison with observational estimates, and improvements in comparison with the predecessor system are documented; for example, for tropical variability and sea ice representation. Introducing an eddy‐permitting grid configuration in the ocean leads to improvements, in particular, in the representation of interior water mass properties in the Atlantic and in the representation of important ocean currents, such as the Agulhas and Equatorial current systems. In general, however, there are more similarities than differences between the two grid configurations, and several shortcomings, known from earlier versions of the coupled model, prevail.
Key Points
documentation the performance of MPIOM in the new MPI‐ESM coupled system
Assessment of impact of ocean resolution
Specific investigation of the Agulhas region
This document constitutes an excerpt of the Technical Design Report for the second stage of the "Any Light Particle Search" (ALPS-II) at DESY as submitted to the DESY PRC in August 2012 and reviewed ...in November 2012. ALPS-II is a "Light Shining through a Wall" experiment which searches for photon oscillations into weakly interacting sub-eV particles. These are often predicted by extensions of the Standard Model and motivated by astrophysical phenomena. The first phases of the ALPS-II project were approved by the DESY management on 21 February, 2013.
The Arctic climate system includes numerous highly interactive small-scale physical processes in the atmosphere, sea ice, and ocean. During and since the International Polar Year 2007–2009, ...significant advances have been made in understanding these processes. Here, these recent advances are reviewed, synthesized, and discussed. In atmospheric physics, the primary advances have been in cloud physics, radiative transfer, mesoscale cyclones, coastal, and fjordic processes as well as in boundary layer processes and surface fluxes. In sea ice and its snow cover, advances have been made in understanding of the surface albedo and its relationships with snow properties, the internal structure of sea ice, the heat and salt transfer in ice, the formation of superimposed ice and snow ice, and the small-scale dynamics of sea ice. For the ocean, significant advances have been related to exchange processes at the ice–ocean interface, diapycnal mixing, double-diffusive convection, tidal currents and diurnal resonance. Despite this recent progress, some of these small-scale physical processes are still not sufficiently understood: these include wave–turbulence interactions in the atmosphere and ocean, the exchange of heat and salt at the ice–ocean interface, and the mechanical weakening of sea ice. Many other processes are reasonably well understood as stand-alone processes but the challenge is to understand their interactions with and impacts and feedbacks on other processes. Uncertainty in the parameterization of small-scale processes continues to be among the greatest challenges facing climate modelling, particularly in high latitudes. Further improvements in parameterization require new year-round field campaigns on the Arctic sea ice, closely combined with satellite remote sensing studies and numerical model experiments.
The authors examine the transition from a seasonally ice-covered Arctic to an Arctic Ocean that is sea ice free all year round under increasing atmospheric CO₂ levels. It is shown that in ...comprehensive climate models, such loss of Arctic winter sea ice area is faster than the preceding loss of summer sea ice area for the same rate of warming. In two of the models, several million square kilometers of winter sea ice are lost within only one decade. It is shown that neither surface albedo nor cloud feedbacks can explain the rapid winter ice loss in the climate model MPI-ESM by suppressing both feedbacks in the model. The authors argue that the large sensitivity of winter sea ice area in the models is caused by the asymmetry between melting and freezing: an ice-free summer requires the complete melt of even the thickest sea ice, which is why the perennial ice coverage decreases only gradually as more and more of the thinner ice melts away. In winter, however, sea ice areal coverage remains high as long as sea ice still forms, and then drops to zero wherever the ocean warms sufficiently to no longer form ice during winter. The loss of basinwide Arctic winter sea ice area, however, is still gradual in most models since the threshold mechanism proposed here is reversible and not associated with the existence of multiple steady states. As this occurs in every model analyzed here and is independent of any specific parameterization, it is likely to be relevant in the real world.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We examine the recovery of Arctic sea ice from prescribed ice‐free summer conditions in simulations of 21st century climate in an atmosphere–ocean general circulation model. We find that ice extent ...recovers typically within two years. The excess oceanic heat that had built up during the ice‐free summer is rapidly returned to the atmosphere during the following autumn and winter, and then leaves the Arctic partly through increased longwave emission at the top of the atmosphere and partly through reduced atmospheric heat advection from lower latitudes. Oceanic heat transport does not contribute significantly to the loss of the excess heat. Our results suggest that anomalous loss of Arctic sea ice during a single summer is reversible, as the ice–albedo feedback is alleviated by large‐scale recovery mechanisms. Hence, hysteretic threshold behavior (or a “tipping point”) is unlikely to occur during the decline of Arctic summer sea‐ice cover in the 21st century.
A seasonal forecast system is presented, based on the global coupled climate model MPI-ESM as used for CMIP5 simulations. We describe the initialisation of the system and analyse its predictive skill ...for surface temperature. The presented system is initialised in the atmospheric, oceanic, and sea ice component of the model from reanalysis/observations with full field nudging in all three components. For the initialisation of the ensemble, bred vectors with a vertically varying norm are implemented in the ocean component to generate initial perturbations. In a set of ensemble hindcast simulations, starting each May and November between 1982 and 2010, we analyse the predictive skill. Bias-corrected ensemble forecasts for each start date reproduce the observed surface temperature anomalies at 2–4 months lead time, particularly in the tropics. Niño3.4 sea surface temperature anomalies show a small root-mean-square error and predictive skill up to 6 months. Away from the tropics, predictive skill is mostly limited to the ocean, and to regions which are strongly influenced by ENSO teleconnections. In summary, the presented seasonal prediction system based on a coupled climate model shows predictive skill for surface temperature at seasonal time scales comparable to other seasonal prediction systems using different underlying models and initialisation strategies. As the same model underlying our seasonal prediction system—with a different initialisation—is presently also used for decadal predictions, this is an important step towards seamless seasonal-to-decadal climate predictions.
Abstract
Direct numerical simulation and laboratory experiments are used to investigate turbulent convection beneath a horizontal ice–water interface. Scaling laws are derived that quantify the ...dependence of the melt rate of the ice on the far-field temperature of the water under purely thermally driven conditions. The scaling laws, the simulations, and the laboratory experiments consistently yield that the melt rate increases by two orders of magnitude, from ⋍10
1
to ⋍10
3
mm day
−1
, as the far-field temperature increases from 4° to 8°C. The strong temperature dependence of the melt rate is explained by analyzing the vertical structure of the flow: For far-field temperatures below 8°C, the flow features a stably stratified, diffusive layer next to the ice that shields it from the warmer, turbulent outer layer. The stratification in the diffusive layer diminishes as the far-field temperature increases and vanishes for far-field temperatures far above 8°C. Possible implications of these results for ice–ocean interfaces are discussed. The drastic melt-rate increase implies that turbulence needs to be considered in the analysis of ice–water interfaces even in shear-free conditions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
We use a 1-D model to study how salinity evolves in Arctic sea ice. To do so, we first explore how sea-ice surface melt and flooding can be incorporated into the 1-D thermodynamic Semi-Adaptive ...Multi-phase Sea-Ice Model (SAMSIM) presented by Griewank and Notz (2013). We introduce flooding and a flushing parametrization which treats sea ice as a hydraulic network of horizontal and vertical fluxes. Forcing SAMSIM with 36 years of ERA-interim atmospheric reanalysis data, we obtain a modelled Arctic sea-ice salinity that agrees well with ice-core measurements. The simulations thus allow us to identify the main drivers of the observed mean salinity profile in Arctic sea ice. Our results show a 1.5–4 g kg−1 decrease of bulk salinity via gravity drainage after ice growth has ceased and before flushing sets in, which hinders approximating bulk salinity from ice thickness beyond the first growth season. In our simulations, salinity interannual variability of first-year ice is mostly restricted to the top 20 cm. We find that ice thickness, thermal resistivity, freshwater column, and stored energy change by less than 5% on average when the full salinity parametrization is replaced with a prescribed salinity profile.
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