Towards the validation of a traceable climate model hierarchies

This is the final version of the article. Available from OUP via the DOI in this recordBackground It is a common practice to use a simple model to explain the mechanisms or processes that occur in a much more complex, complete and computationally expensive model. Many such examples can be found in climate change research. Objective This paper uses two illustrative examples to show how we can quantitatively relate the mechanisms or processes observed in a simple climate model to similar mechanisms in a more complex one. Method A simple model can only explain a more complex solution’s mechanisms if outcomes are tested over a broad range of inputs. By carefully sampling the full set of inputs for both the simple and complex models, we can robustly compare the process or mechanistic outcomes, statistically, between them. Thus, by examining the similarity or differences in the relationship between the inputs and outputs. The method can reject an incorrect simple model. Results The examples are, first, analytic and numerical solutions to the heat equation and, second, the 1948 Stommel model of horizontal ocean circulation and a more complex quasi-geostrophic ocean model. We quantitatively state how similar the simple model’s mechanisms are to the mechanisms in the more complex representation. In addition, when a simple solution may be correct, we give the percentage of the variance of the complex model’s outcomes that is explained by the simple response along with an uncertainty estimate. Conclusion We successfully tested a methodology for robustly quantifying how the physics encapsulated by a simple model of a process may exhibit itself in another, more complex formulation. Suggestions are given as a guide for use of the methodology with more complex and realistic models.Funding: NSF (0851065)

Influence of initial ocean conditions on temperature and precipitation in a coupled climate model’s solution

This is the final version. Available on open access from Copernicus Publications via the DOI in this recordCode and data availability: Model output and software code are available through a request to the authors.This paper describes results of an experiment that perturbed the initial conditions for the ocean’s temperature field of the Community Earth System Model (CESM) with a well defined design. The resulting thirty member ensemble of CESM simulations, each of ten years in length is used to create an emulator (a non-linear regression relating the initial conditions to various outcomes) from the simulators. Through the use of the emulator to expand the output distribution space, we estimate the spatial uncertainties at 10 years for surface air temperature, 25m ocean temperature, precipitation, and rain. Basin averages, outside the tropics, for the uncertainty in the ocean temperature field range between 0.48◦C (Indian Ocean) and 0.87◦C (North Pacific) (two standard deviations). The tropical Pacific uncertainty is the largest due to different phasings of the ENSO signal. Over land areas, the regional temperature uncertainty varies from 1.03◦C (South America) to 10.82◦C (Europe) (two standard deviations). Similarly, the regional average uncertainty in precipitation varies from 0.001 cm/day over Antarctica to 0.163 cm/day over Australia with the global average of 0.075 cm/day. In general, both temperature and precipitation uncertainties are larger over land than over the ocean. A maximum covariance analysis is used to examine how ocean temperatures affect both surface air temperatures and precipitation over land. The analysis shows that the tropical Pacific influences the temperature over North America, but the North America surface temperature is also moderated by the state of the North Pacific outside the tropics. It also indicates which regions show a high degree of variance between the simulations in the ensemble and are, therefore, less predictable. The calculated uncertainties are also compared to an estimate of internal variability within CESM. Finally, the importance of feedback processes on the solution of the simulation over the ten years of the experiment is quantified. These estimates of uncertainty are without the consideration of anthropogenic effect on warming of the atmosphere and ocean.National Science Foundation: NS

Towards the Use of POP in a Global Coupled Navy Prediction System

LONG-TERM GOALS: Development of a global high resolution coupled atmosphere/ocean/ice model that assimilates data providing initial conditions from which forecasts are performed. Additionally, very high-resolution regional air/ocean coupled models will be nested into the global system at key strategic locations.Award Number: N0001401WR2015

The ACPI Project, Element 1: Initializing a Coupled Climate Model from Observed Conditions

A problem for climate change studies with coupled ocean-atmosphere models has been how to incorporate observed initial conditions into the ocean, which holds most of the ‘memory’ of anthropogenic forcing effects. The first difficulty is the lack of comprehensive three-dimensional observations of the current ocean temperature (T) and salinity (S) fields to initialize to. The second problem is that directly imposing observed T and S fields into the model results in rapid drift back to the model climatology, with the corresponding loss of the observed information. Anthropogenic forcing scenarios therefore typically initialize future runs by starting with pre-industrial conditions. However, if the future climate depends on the details of the present climate, then initializing the model to observations may provide more accurate forecasts. Also, this ∼130 yr spin up imposes substantial overhead if only a few decades of predictions are desired. A new technique to address these problems is presented. In lieu of observed T and S, assimilated ocean data were used. To reduce model drift, an anomaly coupling scheme was devised. This consists of letting the model’s climatological (pre-industrial) oceanic and atmospheric heat contents and transports balance each other, while adding on the (much smaller) changes in heat content since the pre-industrial era as anomalies. The result is model drift of no more than 0.2 K over 50 years, significantly smaller than the forced response of 1.0 K. An ensemble of runs with these assimilated initial conditions is then compared to a set spun up from pre-industrial conditions. No systematic differences were found, i.e., the model simulation of the ocean temperature structure in the late 1990s is statistically indistinguishable from the assimilated observations. However, a model with a worse representation of the late 20th century climate might show significant differences if initialized in this way.This work was supported by the Department of Energy under grant DE-FG03– 98ER62505

Global ocean modeling and state estimation in support of climate research

During the last decade it has become obvious that the ocean circulation shows vigorous variability on a wide range of time and space scales and that the concept of a "sluggish" and slowly varying circulation is rather elusive. Increasing emphasis has to be put, therefore, on observing the rapidly changing ocean state on time scales ranging from weeks to decades and beyond, and on understanding the ocean's response to changing atmospheric forcing conditions. As outlined in various strategy and implementation documents (e.g., the implementation plans of WOCE, AMS, CLIVAR, and GODAE) a combination of the global ocean data sets with a state-of-the-art numerical circulation model is required to interpret the various diverse data sets and to produce the best possible estimates of the time-varying ocean circulation. The mechanism of ocean state estimates is a powerful tool for such a "synthesis" of observations, obtained on very complex space-time pattern, into one dynamically consistent picture of the global time-evolving ocean circulation. This process has much in common with ongoing analysis and reanalysis activities in the atmospheric community. But because the ocean is, and will remain for the foreseeable future, substantially under-sampled, the burden put on the modeling and estimations components is substantially larger than in the atmosphere. Moreover, the smaller dynamical eddy scales which need to be properly parameterized or resolved in ocean model simulations, put stringent requirements on computational resources for ongoing and participated climate research

Towards the Use of POP in a Global Coupled Navy Prediction System

Provide realistic high-resolution global ocean and ocean/ice states to Fleet Numerical Meteorological and Oceanographic Center (FNMOC) for initialization of a coupled global air/ocean/ice synoptic prediction system.N0001402WR2012

Large-Scale Forcing of the Agulhas Variability: The Seasonal Cycle

In this article the authors examine the kinematics and dynamics of the seasonal cycle in the western Indian Ocean in an eddy-permitting global simulation [Parallel Ocean Circulation Model, model run 4C (POCM-4C)]. Seasonal changes of the transport of the Agulhas Current are linked to the large-scale circulation in the tropical region. According to the model, the Agulhas Current transport has a seasonal variation with a maximum at the transition between the austral winter and the austral spring and a minimum between the austral summer and the austral autumn. Regional and basin-scale mass balances indicate that although the mean flow of the Agulhas Current has a substantial contribution from the Indonesian Throughflow, there appears to be no dynamical linkage between the seasonal oscillations of these two currents. Instead, evidence was found that the seasonal cycle of the western Indian Ocean is the result of the oscillation of barotropic modes forced directly by the wind

Modeling daily soil salinity dynamics in response to agricultural and environmental changes in coastal Bangladesh

Understanding the dynamics of salt movement in the soil is a prerequisite for devising appropriate management strategies for land productivity of coastal regions, especially low-lying delta regions, which support many millions of farmers around the world. At present, there are no numerical models able to resolve soil salinity at regional scale and at daily time steps. In this research, we develop a novel holistic approach to simulate soil salinization comprising an emulator-based soil salt and water balance calculated at daily time steps. The method is demonstrated for the agriculture areas of coastal Bangladesh (∼20,000 km2). This shows that we can reproduce the dynamics of soil salinity under multiple land uses, including rice crops, combined shrimp and rice farming, as well as non-rice crops. The model also reproduced well the observed spatial soil salinity for the year 2009. Using this approach, we have projected the soil salinity for three different climate ensembles, including relative sea-level rise for the year 2050. Projected soil salinity changes are significantly smaller than other reported projections. The results suggest that inter-season weather variability is a key driver of salinization of agriculture soils at coastal Bangladesh