Generation of SST anomalies in the midlatitudes

Analyses of monthly mean sea surface temperatures (SST) from a hierarchy of global cou- pled ocean-atmosphere models have been carried out with the focus on the midlatitudes (20N-45N). The spectra of the simulated SSTs have been tested against the null hypothe- sis of Hasselmann's stochastic climate model, which assumes an AR(1)-process for the SST variability. It has been found that the spectra of the SST variability in CGCl\/ls with fully dynamical ocean models are significantly different from the AR(1)-process, while the SST variability in an AGCM coupled to a slab ocean is consistent with an AR(1)-process. The deviation of the SST variability in CGCl\/ls with fully dynamical ocean models from the AR(1)-process are not characterized by spectral peaks but are due to a different shape of the spectra. This can be attributed to local air-sea interactions which can be simulated with an AGCM coupled to a slab ocean with dynamical varying mixed layer depth

Large-scale SST variability in the midlatitudes and in the tropical Atlantic

In this these the SST variability of the northern midlatitudes and the tropical Atlantic have been analysed. The analysis has been based on the comparison of the observations with a hierarchy of different coupled simulations. The analysis of the midlatitude SST variability has shown that the large-scale features of the SST variability cannot be simulated by a fixed depth mixed layer ocean model and that the spectral distribution of the SST is significantly different for an AR-l process on time scales from seasons to decades. The processes that are important for these differences are the seasonal variability of the mixed layer depth, the wind induced mixing, which entrains water from the sub-mixed layer ocean, and the heat exchange between the mixed layer and the sub-mixed layer ocean. The observed increase in the SST variance from the interannual to the decadal time scale is due to the heat exchange between the sub-mixed layer ocean and the mixed layer and not, as in the AI I X50 simulation, merely an effect of the integration of atmospheric noise. All these processes can be simulated by the local air-sea interactions in the dynamical ocean mixed layer IUIIX¿unarnàc. The analysis of the seasonal predictability of the SST in the MIX¿,,no ¿.simulation indicates that the knowledge of the actual mixed layer depth is important to predict the SST development in summer and fall. In the analysis of the tropical Atlantic SST variability, it was found that the two dominant SST patterns of the observed SST and in all analysed CGCMs are centred in the northern and in the southern trade wind zones, whereas the correlation between the two patterns is not significantly different from zero. An interhemispheric dipole, or stated differently, an anti-correlation of the SSTs in the northern and southern trade wind zones, which could be important for rainfall anomalies in e.g. north-east BrazTl, does therefore not exist. I conclude that the often cited dipole pattern is an artifact of the EOF analysis technique used. The fact that the simple slab ocean model produces the same pattern, indicates that the SST anomalies are forced by the atmosphere consistent with the Null hypothesis of SST variability. In the final chapter of this work I have introduced a new technique to study the response of the atmosphere to a given SST pattern in a coupled simulation. In this new technique the SST anomaly patterns or historical SST time series is introduced by an additional heat flux into the seasonal mixed layer ocean model. The comparison of the atmospheric response in the coupled simulation with the usual AMIP-type simulation has shown that the response in the midlatitudes can be significantly different and that the response of the atmosphere is very sensible to the structure of the given SST anomaly pattern. In general the new technique seems to be a good tool to study the atmospheric response to SST anomaly pattern in the midlatitudes and instead of introducing a fixed SST pattern or a given historical SST time series, the mixed layer simulation offers many other possibilities

A hydrological cycle model for the Globally Resolved Energy Balance (GREB) model v1.0

This study describes the development of the hydrological cycle model for the Globally Resolved Energy Balance (GREB) model. Starting from a rudimentary hydrological cycle model included in the GREB model, we develop three new models: precipitation, evaporation and horizontal transport of water vapour. Precipitation is modelled based on the actual simulated specific and relative humidity in GREB and the prescribed boundary condition of vertical velocity. The evaporation bulk formula is slightly refined by considering differences in the sensitivity to winds between land and oceans, and by improving the estimates of the wind magnitudes. Horizontal transport of water vapour is improved by approximating moisture convergence by vertical velocity. The new parameterisations are fitted against the Global Precipitation Climatology Project (GPCP) data set and reanalysis data sets (ERA-Interim). The new hydrological cycle model is evaluated against the Coupled Model Intercomparison Project phase 5 (CMIP5) model simulations, reduction in correction terms and by three different sensitivity experiments (annual cycle, El Niño–Southern Oscillation and climate change). The skill of the hydrological cycle model in the GREB model is now within the range of more complex CMIP5 coupled general circulation models and capable of simulating key features of the climate system within the range of uncertainty of CMIP5 model simulations. The results illustrate that the new GREB model's hydrological cycle is a useful model to study the climate's hydrological response to external forcings and also to study inter-model differences or biases.</p

Conceptual deconstruction of the simulated precipitation response to climate change

This is the author accepted manuscript. The final version is available from Springer via the DOI in this recordState-of-the-art climate change projections of the CMIP5 simulations suggest a fairly complex pattern of global precipitation changes, with regions of reduced and enhanced precipitation. Conceptual understanding of these projected precipitation changes is difficult if only based on coupled general circulation model (CGCM) simulations, due to the complexity of these models. In this study we describe a simple deconstruction of the ensemble mean CMIP5 projections based on sensitivity simulations with the globally resolved energy balance (GREB) model. In a series of sensitivity experiments we force the GREB model with four different CMIP5 ensemble mean changes in: surface temperature, evaporation and the vertical atmospheric velocities mean and its standard deviation. The resulting response in the precipitation of the GREB model is very close to the CMIP5 ensemble mean response, suggesting that the precipitation changes can be well represented by a linear combination of these four forcings. The results further provide good insights into the drivers of precipitation change. The GREB model suggests that not one forcing alone can be seen as the main driver, but only the combination of all four changes results in the complex response pattern. However, the dominant forcings are the changes in the large-scale circulation, rather than the pure thermodynamic warming effect. Here, it is interesting to note that changes in high-frequency atmospheric variability of vertical air motion (weather), that are partly independent of the changes in the mean circulation, have a control on the pattern of the time-mean global precipitation changes. The approach presented here provides a powerful basis on which the hydrological cycles of CGCM simulations can be analysed.Australian Research Council (ARC)Newton Fun

An ensemble of AMIP simulations with prescribed land surface temperatures

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&thinsp;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&thinsp;%). 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.</p

Ocean mixedlayer depth: A subsurface proxy for ocean-atmosphere variability

A new criterion, based on the shallowest extreme curvature of near surface layer density or temperature profiles, is established for demarking the mixed layer depth, h mix. Using historical global hydrographic profile data, including conductivity-temperature-depth and expendable bathythermograph data obtained during World Ocean Circulation Experiment, its seasonal variability and monthly to interannual anomalies are computed. Unlike the more commonly used Δ criterion, the new criterion is able to deal with both different vertical resolutions of the data set and a large variety of observed stratification profiles. For about two thirds of the profiles our algorithm produces an h mix/c that is more reliable than the one of the Δ criterion. The uncertainty for h mix/c is ±5 m for high- (<5 m) and ±8 m for low- (<20 m) resolution profiles. A quality index, QImix, which compares the variance of a profile above h mix to the variance to a depth of 1.5 × h mix, shows that for the 70% of the profile data for which a clearly recognizable well-mixed zone exists near the surface, our criterion identifies the depth of the well-mixed zone in all cases. The standard deviation of anomalous monthly h mix/c is typically 20–70% of the long-term mean h mix/c . In the tropical Pacific the monthly mean anomalies of h mix/c are not well correlated with anomalies of sea surface temperature, which indicate that a variety of turbulent processes, other than surface heat fluxes, are important in the upper ocean there. Comparisons between observed h mix/c and Massachusetts Institute of Techonology/ocean general circulation model/Estimating the Circulation and Climate of the Ocean model simulated mixed layer depth indicate that the KPP algorithm captures in general a 30% smaller mixed layer depth than observed

Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response

Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs

Challenges and opportunities for improved understanding of regional climate dynamics

Dynamical processes in the atmosphere and ocean are central to determining the large-scale drivers of regional climate change, yet their predictive understanding is poor. Here, we identify three frontline challenges in climate dynamics where significant progress can be made to inform adaptation: response of storms, blocks and jet streams to external forcing; basin-to-basin and tropical–extratropical teleconnections; and the development of non-linear predictive theory. We highlight opportunities and techniques for making immediate progress in these areas, which critically involve the development of high-resolution coupled model simulations, partial coupling or pacemaker experiments, as well as the development and use of dynamical metrics and exploitation of hierarchies of models