Gregory M. Flato

Investigating the Causes of the Response of the Thermohaline Circulation to Past and Future Climate Changes

The Atlantic thermohaline circulation (THC) is an important part of the earth’s climate system. Previous research has shown large uncertainties in simulating future changes in this critical system. The simulated THC response to idealized freshwater perturbations and the associated climate changes have been intercompared as an activity of World Climate Research Program (WCRP) Coupled Model Intercomparison Project/Paleo-Modeling Intercomparison Project (CMIP/PMIP) committees. This intercomparison among models ranging from the earth system models of intermediate complexity (EMICs) to the fully coupled atmosphere–ocean general circulation models (AOGCMs) seeks to document and improve understanding of the causes of the wide variations in the modeled THC response. The robustness of particular simulation features has been evaluated across the model results. In response to 0.1-S v( 1 Sv 10 6 m 3 s 1 ) freshwater input in the northern North Atlantic, the multimodel ensemble mean THC weakens by 30% after 100 yr. All models simulate some weakening of the THC, but no model simulates a complete shutdown of the THC. The multimodel ensemble indicates that the surface air temperature could present a complex anomaly pattern with cooling south of Greenland and warming over the Barents and Nordic Seas. The Atlantic ITCZ tends to shift southward. In response to 1.0-Sv freshwater input, the THC switches off rapidly in all model simulations. A large cooling occurs over the North Atlantic. The annual mean Atlantic ITCZ moves into the Southern Hemisphere. Models disagree in terms of the reversibility of the THC after its shutdown. In general, the EMICs and AOGCMs obtain similar THC responses and climate changes with more pronounced and sharper patterns in the AOGCMs.

The Canadian Seasonal to Interannual Prediction System. Part I: Models and Initialization

AbstractThe Canadian Seasonal to Interannual Prediction System (CanSIPS) became operational at Environment Canadas Canadian Meteorological Centre (CMC) in December 2011, replacing CMCs previous two-tier system. CanSIPS is a two-model forecasting system that combines ensemble forecasts from the Canadian Centre for Climate Modeling and Analysis (CCCma) Coupled Climate Model, versions 3 and 4 (CanCM3 and CanCM4, respectively). Mean climate as well as climate trends and variability in these models are evaluated in freely running historical simulations. Initial conditions for CanSIPS forecasts are obtained from an ensemble of coupled assimilation runs. These runs assimilate gridded atmospheric analyses by means of a procedure that resembles the incremental analysis update technique, but introduces only a fraction of the analysis increment in order that differences between ensemble members reflect the magnitude of observational uncertainties. The land surface is initialized through its response to the assimil...

Enhanced Climate Change and Its Detection over the Rocky Mountains

Results from an ensemble of climate change experiments with increasing greenhouse gas and aerosols using the Canadian Centre for Climate Modelling and Analysis Coupled Climate Model are presented with a focus on surface quantities over the Rocky Mountains. There is a marked elevation dependency of the simulated surface screen temperature increase over the Rocky Mountains in the winter and spring seasons, with more pronounced changes at higher elevations. The elevation signal is linked to a rise in the snow line in the winter and spring seasons, which amplifies the surface warming via the snow-albedo feedback. Analysis of the winter surface energy budget shows that large changes in the solar component of the radiative input are the direct consequence of surface albedo changes caused by decreasing snow cover. Although the warming signal is enhanced at higher elevations, a two-way analysis of variance reveals that the elevation effect has no potential for early climate change detection. In the early stages of surface warming the elevation effect is masked by relatively large noise, so that the signal-to-noise ratio over the Rocky Mountains is no larger than elsewhere. Only after significant continental-scale warming does the local Rocky Mountain signal begin to dominate the pattern of climate change over western North America (and presumably also the surrounding ecosystems and hydrological networks).

A regime view of northern hemisphere atmospheric variability and change under global warming

The leading mode of wintertime variability in Northern Hemisphere sea level pressure (SLP) is the Arctic Oscillation (AO). It is usually obtained using linear principal component analysis, which produces the optimal, although somewhat restrictive, linear approximation to the SLP data. Here we use a recently introduced nonlinear principal component analysis to find the optimal nonlinear approximation to SLP data produced by a 1001 year integration of the CCCma coupled general circulation model (CGCM1). This approximations associated time series is strongly bimodal and partitions the data into two distinct regimes. The first and more persistent regime describes a standing oscillation whose signature in the mid-troposphere is alternating amplification and attenuation of the climatological ridge over Northern Europe, with associated decreasing and increasing daily variance over Northern Eurasia. The second and more episodic regime describes a split-flow south of Greenland with much enhanced daily variance in the Arctic. In a 500 year integration with atmospheric CO2 stabilized at concentrations projected for year 2100, the occupation statistics of these preferred modes of variability change, such that the episodic split-flow regime occurs less frequently while the standing oscillation regime occurs more frequently.

The Effect of Terrestrial Photosynthesis Down Regulation on the Twentieth-Century Carbon Budget Simulated with the CCCma Earth System Model

Abstract The simulation of atmospheric–land–ocean CO2 exchange for the 1850–2000 period offers the possibility of testing and calibrating the carbon budget in earth system models by comparing the simulated changes in atmospheric CO2 concentration and in land and ocean uptake with observation-based information. In particular, some of the uncertainties associated with the treatment of land use change (LUC) and the role of down regulation in affecting the strength of CO2 fertilization for terrestrial photosynthesis are assessed using the Canadian Centre for Climate Modelling and Analysis Earth System Model (CanESM1). LUC emissions may be specified as an external source of CO2 or calculated interactively based on estimated changes in crop area. The evidence for photosynthetic down regulation is reviewed and an empirically based representation is implemented and tested in the model. Four fully coupled simulations are performed: with and without terrestrial photosynthesis down regulation and with interactively ...

Application of the Canadian regional climate model to the Laurentian great lakes region: Implementation of a lake model

Abstract This study reports on the implementation of an interactive mixed‐layer/thermodynamic‐ice lake model coupled with the Canadian Regional Climate Model (CRCM). For this application the CRCM, which uses a grid mesh of 45 km on a polar stereographic projection, 10 vertical levels, and a timestep of 15 min, is nested with the second generation Canadian General Circulation Model (GCM) simulated output. A numerical simulation of the climate of eastern North America, including the Laurentian Great Lakes, is then performed in order to evaluate the coupled model. The lakes are represented by a “mixed layer” model to simulate the evolution of the surface water temperature, and a thermodynamic ice model to simulate evolution of the ice cover. The mixed‐layer depth is allowed to vary spatially. Lake‐ice leads are parametrized as a function of ice thickness based on observations. Results from a 5‐year integration show that the coupled CRCM/lake model is capable of simulating the seasonal evolution of surface temperature and ice cover in the Great Lakes. When compared with lake climatology, the simulated mean surface water temperature agrees within 0.12°C on average. The seasonal evolution of the lake‐ice cover is realistic but the model tends to underestimate the monthly mean ice concentration on average. The simulated winter lake‐induced precipitation is also shown, and snow accumulation patterns on downwind shores of the lakes are found to be realistic when compared with observations.

Sea Ice Response to Wind Forcing from AMIP Models

Abstract The Arctic surface circulation simulated by atmospheric general circulation models is assessed in the context of driving sea ice motion. A sea ice model is forced by geostrophic winds from eight models participating in the first Atmospheric Model Intercomparison Project (AMIP1), and the results are compared to simulations with the sea ice model forced by observed winds. The mean sea level pressure in the AMIP models is generally too high over the Arctic Ocean, except in the Beaufort and Chukchi Seas, where it is too low. This pattern creates anomalous winds that tend to transport too much ice away from the coast of Greenland and the Canadian Archipalego, and into the East Siberian Sea, producing a pattern of ice thickness in the Arctic that is rotated by roughly 180° relative to what is expected based on observations. AMIP winds also drive too little ice transport through Fram Strait and too much transport east of Svalbard by way of the Barents Sea. These errors in ice thickness and transport inf...

Sea Ice in the Canadian Arctic Archipelago: Modeling the Past (1950–2004) and the Future (2041–60)

Abstract Considering the recent losses observed in Arctic sea ice and the anticipated future warming due to anthropogenic greenhouse gas emissions, sea ice retreat in the Canadian Arctic Archipelago (CAA) is expected and indeed is already being observed. As most global climate models do not resolve the CAA region, a fine-resolution ice–ocean regional model is developed and used to make a projection of future changes in the CAA sea ice. Results from a historical run (1950–2004) are used to evaluate the model. The model does well in representing observed sea ice spatial and seasonal variability, but tends to underestimate summertime ice cover. The model results for the future (2041–60) show little change in wintertime ice concentrations from the past, but summertime ice concentrations decrease by 45%. The ice thickness is projected to decrease by 17% in the winter and by 36% in summer. Based on this study, a completely ice-free CAA is unlikely by the year 2050, but the simulated ice retreat suggests that th...

One hundred years of Arctic surface temperature variation due to anthropogenic influence

Observations show that Arctic-average surface temperature increased from 1900 to 1940, decreased from 1940 to 1970, and increased from 1970 to present. Here, using new observational data and improved climate models employing observed natural and anthropogenic forcings, we demonstrate that contributions from greenhouse gas and aerosol emissions, along with explosive volcanic eruptions, explain most of this observed variation in Arctic surface temperature since 1900. In addition, climate model simulations without natural and anthropogenic forcings indicate very low probabilities that the observed trends in each of these periods were due to internal climate variability alone. Arctic climate change has important environmental and economic impacts and these results improve our understanding of past Arctic climate change and our confidence in future projections.

A particle‐in‐cell sea‐ice model

Abstract Fronts or discontinuities in geophysical flows are often smoothed in numerical models owing to artificial diffusion and dispersion introduced by the advection algorithm. This is a particular problem in operational sea‐ice forecasting where the position of the ice edge must be accurately predicted. The particle‐in‐cell (PIC) scheme avoids artificial smoothing by partitioning the ice volume into individual particles whose motion is integrated in a Lagrangian sense. The velocity field is obtained by solving the momentum equation on an underlying fixed Eulerian grid. Comparisons between the PIC scheme and more conventional advection algorithms are presented using both idealized and observed forcing fields. An advantage of the PIC model is that the resolution of the underlying Eulerian grid (on which the computationally expensive momentum equation is solved) can be chosen to represent the spatial variability of the forcing fields (winds and currents) rather than to minimize advection errors. A large n...