Quantifying the Predictive Skill in Long-Range Forecasting. Part II: Model Error in Coarse-Grained Markov Models with Application to Ocean-Circulation Regimes

2012 ◽  
Vol 25 (6) ◽  
pp. 1814-1826 ◽  
Author(s):  
Dimitrios Giannakis ◽  
Andrew J. Majda
Keyword(s):  
Long Range ◽  
Ocean Circulation ◽  
Climate Models ◽  
Markov Models ◽  
Low Frequency ◽  
Model Error ◽  
Ocean Model ◽  
Coarse Grained ◽  
Predictive Skill ◽  
Relaxation To Equilibrium

Abstract An information-theoretic framework is developed to assess the predictive skill and model error in imperfect climate models for long-range forecasting. Here, of key importance is a climate equilibrium consistency test for detecting false predictive skill, as well as an analogous criterion describing model error during relaxation to equilibrium. Climate equilibrium consistency enforces the requirement that long-range forecasting models should reproduce the climatology of prediction observables with high fidelity. If a model meets both climate consistency and the analogous criterion describing model error during relaxation to equilibrium, then relative entropy can be used as an unbiased superensemble measure of the model’s skill in long-range coarse-grained forecasts. As an application, the authors investigate the error in modeling regime transitions in a 1.5-layer ocean model as a Markov process and identify models that are strongly persistent but their predictive skill is false. The general techniques developed here are also useful for estimating predictive skill with model error for Markov models of low-frequency atmospheric regimes.

Quantifying the Predictive Skill in Long-Range Forecasting. Part I: Coarse-Grained Predictions in a Simple Ocean Model

2012 ◽  
Vol 25 (6) ◽  
pp. 1793-1813 ◽  
Author(s):  
Dimitrios Giannakis ◽  
Andrew J. Majda
Keyword(s):  
Long Range ◽  
Principal Component ◽  
Coarse Graining ◽  
Ocean Model ◽  
Coarse Grained ◽  
Ensemble Prediction ◽  
Information Theoretic ◽  
Predictive Skill ◽  
Mixing Dynamics ◽  
State Models

Abstract An information-theoretic framework is developed to assess the long-range coarse-grained predictive skill in a perfect-model environment. Central to the scheme is the notion that long-range forecasting involves regimes; specifically, that the appropriate initial data for ensemble prediction is the affiliation of the system to a coarse-grained partition of phase space representing regimes. The corresponding ensemble prediction probabilities, which are computable using ergodic signals from the model, are then used to quantify through relative entropy the information beyond climatology in the partition. As an application, the authors study the predictability of circulation regimes in an equivalent barotropic double-gyre ocean model using a partition algorithm based on K-means clustering and running-average coarse graining. Besides the established rolled up and extensional phases of the eastward jet, optimal partitions for triennial-scale forecasts feature a jet configuration dominated by the second empirical orthogonal function (EOF) of the streamfunction, as well as phases in which the jet interacts with eddies in higher EOFs. Due to mixing dynamics, the skill beyond three-state models is lost for forecast lead times longer than three years, but significant skill remains in the energy and the leading principal component of the streamfunction for septennial forecasts.

scholarly journals LGM climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity

2020 ◽  
Author(s):  
Jiang Zhu ◽  
Christopher J. Poulsen
Keyword(s):  
Ocean Circulation ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Model Simulation ◽  
Ice Sheets ◽  
Ocean Model ◽  
Ocean Dynamics ◽  
True Value ◽  
Climate Forcings

Abstract. Equilibrium climate sensitivity (ECS) has been directly estimated using reconstructions of past climates that are different than today’s. A challenge to this approach is that temperature proxies integrate over the timescales of the fast feedback processes (e.g. changes in water vapor, snow, and clouds) that are captured in ECS as well as the slower feedback processes (e.g. changes in ice sheets and ocean circulation) that are not. A way around this issue is to treat the slow feedbacks as climate forcings and independently account for their impact on global temperature. Here we conduct a suite of Last Glacial Maximum (LGM) simulations using the Community Earth System Model version 1.2 (CESM1.2) to quantify the forcing and efficacy of land ice sheets (LIS) and greenhouse gases (GHG) in order to estimate ECS. Our forcing and efficacy quantification adopts the effective radiative forcing (ERF) and adjustment framework and provides a complete accounting for the radiative, topographic, and dynamical impacts of LIS on surface temperatures. ERF and efficacy of LGM LIS are −3.2 W m−2 and 1.1, respectively. The larger-than-unity efficacy is caused by the relatively larger temperature changes over land and the Northern Hemisphere subtropical oceans than those in response to a doubling of atmospheric CO2. The subtropical SST response is linked to LIS-induced wind changes and feedbacks in ocean-atmosphere coupling and clouds. ERF and efficacy of LGM GHG are −2.8 W m−2 and 0.9, respectively. The lower efficacy is primarily attributed to a smaller cloud feedback at colder temperatures. Our simulations further demonstrate that the direct ECS calculation using the forcing, efficacy, and temperature response in CESM1.2 overestimates the true value in the model by 25 % due to the neglect of slow ocean dynamical feedback. This is supported by the greater cooling (6.8 °C) in a fully coupled LGM simulation than that (5.3 °C) in a slab ocean model simulation with ocean dynamics disabled. The majority (67 %) of the ocean dynamical feedback is attributed to dynamical changes in the Southern Ocean, where interactions between ocean stratification, heat transport, and sea-ice cover are found to amplify the LGM cooling. Our study demonstrates the value of climate models in the quantification of climate forcings and the ocean dynamical feedback, which is necessary for an accurate direct ECS estimation.

scholarly journals Simulating Southern Hemisphere extra-tropical climate variability with an idealized coupled atmosphere-ocean model

2011 ◽  
Vol 4 (3) ◽  
pp. 1907-1940
Author(s):  
H. Kurzke ◽  
M. V. Kurgansky ◽  
K. Dethloff ◽  
D. Handorf ◽  
D. Olbers ◽  
...  
Keyword(s):  
Climate Variability ◽  
Southern Hemisphere ◽  
Ocean Circulation ◽  
Low Frequency ◽  
Coupled Model ◽  
Circulation Model ◽  
Ocean Model ◽  
Decadal Climate Variability ◽  
Basic Model ◽  
Physics Model

Abstract. The design and implementation of a simplified coupled atmosphere-ocean model over mid and high Southern Hemisphere latitudes are described. The development of the model is motivated by the clear indications of important low-frequency variability of extratropical origin in atmosphere-only models and the crucial role of atmosphere-ocean interaction in altering and shaping the climate variability on decadal and multidecadal time-scales. The basic model consists of an idealized quasi-geostrophic model of Southern Hemisphere's wintertime atmospheric circulation coupled to a general ocean circulation model with simplified physics. Model spin-up is described, some basic descriptors of the model climatology are discussed, and it is argued that the model exhibits skill in reproducing essential features of decadal and multi-decadal climate variability in the extratropical Southern Hemisphere. Notably, 1000 yr long coupled model simulations reveal sea surface temperature fluctuations on the timescale of several decades in the Antarctic Circumpolar Current region.

scholarly journals Neodymium isotopes in the ocean model of the Community Earth System Model (CESM1.3)

2017 ◽  
Author(s):  
Sifan Gu ◽  
Zhengyu Liu ◽  
Alexandra Jahn ◽  
Johannes Rempfer ◽  
Jiaxu Zhang ◽  
...  
Keyword(s):  
Ocean Circulation ◽  
Climate Models ◽  
Marine Ecosystem ◽  
Ocean Model ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Promising Tool ◽  
Boundary Source ◽  
Community Earth System Model

Abstract. Neodymium (Nd) isotope ratio (εNd) is a quasi-conservative water mass tracer and has been used increasingly as paleoclimate proxy to indicate the past evolution of ocean circulation. However, there are many uncertainties in interpreting εNd reconstructions. For the purposes of direct comparison between climate models and proxy reconstructions, we implement Nd isotopes (143Nd and 144Nd) in the ocean model of the Community Earth System Model (CESM). Two versions of Nd tracers are implemented: one is the "abiotic" Nd in which the particle fields are prescribed as the particle climatology generated by the marine ecosystem module of the CESM under present day forcing; the other is the "biotic" Nd that is coupled with the marine ecosystem module. Under present day climate forcing, our model is able to simulate both Nd concentrations and εNd in good agreement with available observations. Also, Nd concentration and εNd in our model show similar sensitivities to the total boundary source and the ratio between particle related Nd and dissolved Nd as in previous modeling study (Rempfer et al., 2011). Therefore, our Nd-enabled ocean model provides a promising tool to study past changes in ocean and climate.

scholarly journals Synthesis and evaluation of historical meridional heat transport from midlatitudes towards the Arctic

2019 ◽  
Author(s):  
Yang Liu ◽  
Jisk Attema ◽  
Ben Moat ◽  
Wilco Hazeleger
Keyword(s):  
Climate Variability ◽  
Climate Models ◽  
Energy Transport ◽  
Low Frequency ◽  
Temporal Variations ◽  
Ocean Model ◽  
Arctic Climate ◽  
The Arctic ◽  
Meridional Heat Transport ◽  
Sea Ice Concentration

Abstract. Meridional Energy Transport (MET), both in the atmosphere (AMET) and ocean (OMET), has significant impact on the climate in the Arctic. In this study, we quantify AMET and OMET at subpolar latitudes from six reanalyses datasets. We investigate the differences between the datasets and we check the coherence between MET and the Arctic climate variability from annual to interannual scales. The results indicate that, although the mean transport in all datasets agree well, the spatial distribution and temporal variations of AMET and OMET differ substantially among the reanalysis datasets. For the ocean, only after 2010 the low-frequency signals for all reanalyses products agree well. A further comparison with observed heat transports at 26.5° N and the subpolar Atlantic, and a high-resolution ocean model hindcast confirm that the OMET estimated from reanalyses are consistent with independent observations. For the atmosphere, the variations among reanalyses datasets are large. This can be attributed to differences in temperature transport. A further analysis of linkages between the Arctic climate variability and AMET shows that atmospheric reanalyses differ substantially from each other. Among all the chosen atmospheric products, ERA-Interim results are most consistent with results obtained with coupled climate models. For the ocean, ORAS4 and SODA3 agree well on the relation between OMET and sea ice concentration (SIC), while GLORYS2V3 deviates from those data sets. Our study suggests, since the reanalyses products are not designed for the quantification of energy transport, the AMET and OMET estimated from reanalyses should be used with great care, especially when studying variability and interactions between the Arctic and midlatitudes beyond annual time scales.

scholarly journals A Lagrangian Ocean Model for Climate Studies

Climate ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 41 ◽  
Author(s):  
Patrick Haertel
Keyword(s):  
Partial Differential Equations ◽  
Differential Equations ◽  
Ocean Circulation ◽  
Climate Models ◽  
Atmospheric Model ◽  
Ocean Model ◽  
Partial Differential ◽  
Mass Distributions ◽  
Global Atmospheric Model ◽  
Basin Scale

Most weather and climate models simulate circulations by numerically approximating a complex system of partial differential equations that describe fluid flow. These models also typically use one of a few standard methods to parameterize the effects of smaller-scale circulations such as convective plumes. This paper discusses the continued development of a radically different modeling approach. Rather than solving partial differential equations, the author’s Lagrangian models predict the motions of individual fluid parcels using ordinary differential equations. They also use a unique convective parameterization, in which the vertical positions of fluid parcels are rearranged to remove convective instability. Previously, a global atmospheric model and basin-scale ocean models were developed with this approach. In the present study, components of these models are combined to create a new global Lagrangian ocean model (GLOM), which will soon be coupled to a Lagrangian atmospheric model. The first simulations conducted with the GLOM examine the contribution of interior tracer mixing to ocean circulation, stratification, and water mass distributions, and they highlight several special model capabilities: (1) simulating ocean circulations without numerical diffusion of tracers; (2) modeling deep convective transports at low resolution; and (3) identifying the formation location of ocean water masses and water pathways.

scholarly journals Ensemble forecasting greatly expands the prediction horizon for ocean mesoscale variability

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Prasad G. Thoppil ◽  
Sergey Frolov ◽  
Clark D. Rowley ◽  
Carolyn A. Reynolds ◽  
Gregg A. Jacobs ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Mesoscale Dynamics ◽  
Predictive Skill ◽  
Freshwater Transport ◽  
Prediction Horizon ◽  
Initial Location ◽  
Ocean Models ◽  
High Uncertainty

AbstractMesoscale eddies dominate energetics of the ocean, modify mass, heat and freshwater transport and primary production in the upper ocean. However, the forecast skill horizon for ocean mesoscales in current operational models is shorter than 10 days: eddy-resolving ocean models, with horizontal resolution finer than 10 km in mid-latitudes, represent mesoscale dynamics, but mesoscale initial conditions are hard to constrain with available observations. Here we analyze a suite of ocean model simulations at high (1/25°) and lower (1/12.5°) resolution and compare with an ensemble of lower-resolution simulations. We show that the ensemble forecast significantly extends the predictability of the ocean mesoscales to between 20 and 40 days. We find that the lack of predictive skill in data assimilative deterministic ocean models is due to high uncertainty in the initial location and forecast of mesoscale features. Ensemble simulations account for this uncertainty and filter-out unconstrained scales. We suggest that advancements in ensemble analysis and forecasting should complement the current focus on high-resolution modeling of the ocean.

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