[1] The Terra Nova Bay (TNB) and Ross Ice Shelf (RIS) polynyas are simulated using a coupled atmosphere-sea ice model in winter 2002 and summer 2000. The atmospheric component of the model is compared with Automatic Weather Stations (AWS) data and shows a significantly increased skill over the ECMWF atmospheric variables. During winter, the sea ice production in TNB is largely determined by katabatic winds. We estimate the monthly production rate to be 8.7 km 3 /month of sea ice during winter. In early summer (November), the katabatics are weaker and the sea ice production is more influenced by the synoptic wind. The net sea ice production is weaker during November at 1.2 km 3 /month. The summer production is characterized by a diurnal cycle of melt and sea ice formation. For small coastal polynyas, like TNB, it is important to resolve all the glacier valleys accurately. Increasing the model resolution by a factor of five leads to a doubling of the sea ice formation during winter simulations due to the point intensity of the katabatics winds. For open coastal polynyas, like RIS polynya, sea ice production is largely controlled by synoptic winds and resolution is less important. The RIS polynya production is 61.3 km 3 during winter and 19.1 km 3 during November. These results are comparable to RIS polynya observations. Although the TNB polynya has a smaller winter sea ice production, the sea ice rate of growth per unit area is 1.6 times that of the RIS polynya.
Key Points Spatial variability in the land‐atmosphere coupling defines local heatwave sensitivity to antecedent land surface conditions Land‐driven coupling regions experience a higher heatwave day frequency with temperatures sensitive to prior soil moisture conditions Antecedent soil moisture anomaly rather than drying rate 2 weeks prior to a heatwave has a longer impact on heatwave temperatures
Abstract. General circulation models (GCMs) are routinely run under Atmospheric Modelling Intercomparison Project (AMIP) conditions with prescribed sea surface temperatures (SSTs) and sea ice concentrations (SICs) from observations. These AMIP simulations are often used to evaluate the role of the land and/or atmosphere in causing the development of systematic errors in such GCMs. Extensions to the original AMIP experiment have also been developed to evaluate the response of the global climate to increased SSTs (prescribed) and carbon dioxide (CO2) as part of the Cloud Feedback Model Intercomparison Project (CFMIP). None of these international modelling initiatives has undertaken a set of experiments where the land conditions are also prescribed, which is the focus of the work presented in this paper. Experiments are performed initially with freely varying land conditions (surface temperature, and soil temperature and moisture) under five different configurations (AMIP, AMIP with uniform 4 K added to SSTs, AMIP SST with quadrupled CO2, AMIP SST and quadrupled CO2 without the plant stomata response, and increasing the solar constant by 3.3 %). Then, the land surface temperatures from the free land experiments are used to perform a set of “AMIP prescribed land” (PL) simulations, which are evaluated against their free land counterparts. The PL simulations agree well with the free land experiments, which indicates that the land surface is prescribed in a way that is consistent with the original free land configuration. Further experiments are also performed with different combinations of SSTs, CO2 concentrations, solar constant and land conditions. For example, SST and land conditions are used from the AMIP simulation with quadrupled CO2 in order to simulate the atmospheric response to increased CO2 concentrations without the surface temperature changing. The results of all these experiments have been made publicly available for further analysis. The main aims of this paper are to provide a description of the method used and an initial validation of these AMIP prescribed land experiments.
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