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Projected Oceanographical Changes in the Baltic Sea until 2100  

H.E. Markus Meier and Sofia Saraiva

In this article, the concepts and background of regional climate modeling of the future Baltic Sea are summarized and state-of-the-art projections, climate change impact studies, and challenges are discussed. The focus is on projected oceanographic changes in future climate. However, as these changes may have a significant impact on biogeochemical cycling, nutrient load scenario simulations in future climates are briefly discussed as well. The Baltic Sea is special compared to other coastal seas as it is a tideless, semi-enclosed sea with large freshwater and nutrient supply from a partly heavily populated catchment area and a long response time of about 30 years, and as it is, in the early 21st century, warming faster than any other coastal sea in the world. Hence, policymakers request the development of nutrient load abatement strategies in future climate. For this purpose, large ensembles of coupled climate–environmental scenario simulations based upon high-resolution circulation models were developed to estimate changes in water temperature, salinity, sea-ice cover, sea level, oxygen, nutrient, and phytoplankton concentrations, and water transparency, together with uncertainty ranges. Uncertainties in scenario simulations of the Baltic Sea are considerable. Sources of uncertainties are global and regional climate model biases, natural variability, and unknown greenhouse gas emission and nutrient load scenarios. Unknown early 21st-century and future bioavailable nutrient loads from land and atmosphere and the experimental setup of the dynamical downscaling technique are perhaps the largest sources of uncertainties for marine biogeochemistry projections. The high uncertainties might potentially be reducible through investments in new multi-model ensemble simulations that are built on better experimental setups, improved models, and more plausible nutrient loads. The development of community models for the Baltic Sea region with improved performance and common coordinated experiments of scenario simulations is recommended.


Regional Climate Modeling and Air-Sea Coupling  

Corinna Schrum

Regional models were originally developed to serve weather forecasting and regional process studies. Typical simulations encompass time periods in the order of days or weeks. Thereafter regional models were also used more and more as regional climate models for longer integrations and climate change downscaling. Regional climate modeling or regional dynamic downscaling, which are used interchangeably, developed as its own branch in climate research since the end of the 1990s out of the need to bridge the obvious inconsistencies at the interface of global climate research and climate impact research. The primary aim of regional downscaling is to provide consistent regional climate change scenarios with relevant spatial resolution to serve detailed climate impact assessments. Similar to global climate modeling, the early attempts at regional climate modeling were based on uncoupled atmospheric models or stand-alone ocean models, an approach that is still maintained as the most common on the regional scale. However, this approach has some fundamental limitations, since regional air-sea interaction remains unresolved and regional feedbacks are neglected. This is crucial when assessing climate change impacts in the coastal zone or the regional marine environment. To overcome these limitations, regional climate modeling is currently in a transition from uncoupled regional models into coupled atmosphere-ocean models, leading to fully integrated earth system models. Coupled ice-ocean-atmosphere models have been developed during the last decade and are currently robust and well established on the regional scale. Their added value has been demonstrated for regional climate modeling in marine regions, and the importance of regional air-sea interaction became obvious. Coupled atmosphere-ice-ocean models, but also coupled physical-biogeochemical modeling approaches are increasingly used for the marine realm. First attempts to couple these two approaches together with land surface models are underway. Physical coupled atmosphere-ocean modeling is also developing further and first model configurations resolving wave effects at the atmosphere-ocean interface are now available. These new developments now open up for improved regional assessment under broad consideration of local feedbacks and interactions between the regional atmosphere, cryosphere, hydrosphere, and biosphere.


Regional Dynamical Downscaling  

Filippo Giorgi

Dynamical downscaling has been used for about 30 years to produce high-resolution climate information for studies of regional climate processes and for the production of climate information usable for vulnerability, impact assessment and adaptation studies. Three dynamical downscaling tools are available in the literature: high-resolution global atmospheric models (HIRGCMs), variable resolution global atmospheric models (VARGCMs), and regional climate models (RCMs). These techniques share their basic principles, but have different underlying assumptions, advantages and limitations. They have undergone a tremendous growth in the last decades, especially RCMs, to the point that they are considered fundamental tools in climate change research. Major intercomparison programs have been implemented over the years, culminating in the Coordinated Regional climate Downscaling EXperiment (CORDEX), an international program aimed at producing fine scale regional climate information based on multi-model and multi-technique approaches. These intercomparison projects have lead to an increasing understanding of fundamental issues in climate downscaling and in the potential of downscaling techniques to provide actionable climate change information. Yet some open issues remain, most notably that of the added value of downscaling, which are the focus of substantial current research. One of the primary future directions in dynamical downscaling is the development of fully coupled regional earth system models including multiple components, such as the atmosphere, the oceans, the biosphere and the chemosphere. Within this context, dynamical downscaling models offer optimal testbeds to incorporate the human component in a fully interactive way. Another main future research direction is the transition to models running at convection-permitting scales, order of 1–3 km, for climate applications. This is a major modeling step which will require substantial development in research and infrastructure, and will allow the description of local scale processes and phenomena within the climate change context. Especially in view of these future directions, climate downscaling will increasingly constitute a fundamental interface between the climate modeling and end-user communities in support of climate service activities.


The NSF’s Role in Climate Science  

Anjuli S. Bamzai

In the years following the Second World War, the U.S. government played a prominent role in the support of basic scientific research. The National Science Foundation (NSF) was created in 1950 with the primary mission of supporting fundamental science and engineering, excluding medical sciences. Over the years, the NSF has operated from the “bottom up,” keeping close track of research around the United States and the world while maintaining constant contact with the research community to identify ever-moving horizons of inquiry. In the 1950s the field of meteorology was something of a poor cousin to the other branches of science; forecasting was considered more of trade than a discipline founded on sound theoretical foundations. Realizing the importance of the field to both the economy and national security, the NSF leadership made a concerted effort to enhance understanding of the global atmospheric circulation. The National Center for Atmospheric Research (NCAR) was established to complement ongoing research efforts in academic institutions; it has played a pivotal role in providing observational and modeling tools to the emerging cadre of researchers in the disciplines of meteorology and atmospheric sciences. As understanding of the predictability of the coupled atmosphere-ocean system grew, the field of climate science emerged as a natural outgrowth of meteorology, oceanography, and atmospheric sciences. The NSF played a leading role in the implementation of major international programs such as the International Geophysical Year (IGY), the Global Weather Experiment, the World Ocean Circulation Experiment (WOCE) and Tropical Ocean Global Atmosphere (TOGA). Through these programs, understanding of the coupled climate system comprising atmosphere, ocean, land, ice-sheet, and sea ice greatly improved. Consistent with its mission, the NSF supported projects that advanced fundamental knowledge of forcing and feedbacks in the coupled atmosphere-ocean-land system. Research projects have included theoretical, observational, and modeling studies of the following: the general circulation of the stratosphere and troposphere; the processes that govern climate; the causes of climate variability and change; methods of predicting climate variations; climate predictability; development and testing of parameterization of physical processes; numerical methods for use in large-scale climate models; the assembly and analysis of instrumental and/or modeled climate data; data assimilation studies; and the development and use of climate models to diagnose and simulate climate variability and change. Climate scientists work together on an array of topics spanning time scales from the seasonal to the centennial. The NSF also supports research on the natural evolution of the earth’s climate on geological time scales with the goal of providing a baseline for present variability and future trends. The development of paleoclimate data sets has resulted in longer term data for evaluation of model simulations, analogous to the evaluation using instrumental observations. This has enabled scientists to create transformative syntheses of paleoclimate data and modeling outcomes in order to understand the response of the longer-term and higher magnitude variability of the climate system that is observed in the geological records. The NSF will continue to address emerging issues in climate and earth-system science through balanced investments in transformative ideas, enabling infrastructure and major facilities to be developed.


Uncertainty Quantification in Multi-Model Ensembles  

Benjamin Mark Sanderson

Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification. Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.