Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO₂ concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2–5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6–9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2–5 years and 6–9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6–9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.     

A framework for evaluating air quality models

This paper describes a framework to evaluate air quality model predictions against observations. We propose the following relationship between observations and predictions from an adequate model% MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGabm4qayaaja% WaaSbaaSqaaiaaicdaaeqaamXvP5wqonvsaeHbfv3ySLgzaGqbaOGa% e8hkaGIaamiEamaaBaaaleaacaaIXaaabeaakiaacYcacaWG4bWaaS% baaSqaaiaaikdaaeqaaOGae8xkaKIaeyypa0Jabm4qayaajaWaaSba% aSqaaiaadchaaeqaaOGae8hkaGIaamiEamaaBaaaleaacaaIXaaabe% aakiab-LcaPiab-TcaRiabew7aLjab-HcaOiaadIhadaWgaaWcbaGa% aGOmaaqabaGccqWFPaqkaaa!4F93!\[\hat C_0 (x_1 ,x_2 ) = \hat C_p (x_1 ) + \varepsilon (x_2 )\],where x ₁ refers to the inputs used in the model prediction C _p(x ₁), and x ₂denotes unknown variables which affect the observed concentration C ₀. The hats associated with C _pand C ₀denote transformations to convert the residual to a white noise sequence which is normally distributed. In this paper we assume % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGabm4qayaaja% GaeyyyIORaciiBaiaac6gacaWGdbaaaa!3B39!\[\hat C \equiv \ln C\].The standard deviation of determines the expected deviation between model prediction and observation. The purpose of model improvement is to make this deviation as small as possible.The formalism we have proposed is applied to the evaluation of two models developed by this author. We show how careful analysis of residuals can lead to improvements in the model. We have also estimated for each of the models.In the last part of the part of the paper we show how the statistics of can be used to interpret model predictions.     

A radar-based verification of precipitation forecast for local convective storms

Local flash flood storms with a rapid hydrological response are a real challenge for quantitative precipitation forecasting (QPF). It is relevant to assess space domains, to which the QPF approaches are applicable. In this paper an attempt is made to evaluate the forecasting capability of a high-resolution numerical weather prediction (NWP) model by means of area-related QPF verification. The results presented concern two local convective events, which occurred in the Czech Republic (CR) on 13 and 15 July 2002 and caused local flash floods. We used the LM COSMO model (Lokall Model of the COSMO consortium) adapted to the horizontal resolution of 2.8 km over a model domain covering the CR. The 18 h forecast of convective precipitation was verified by using radar rainfall totals adjusted to the measured rain gauge data. The grid point-related root mean square error (RMSE) value was calculated over a square around the grid point under the assumption that rainfall values were randomly distributed within the square. The forecast accuracy was characterized by the mean RMSE over the whole verification domain. We attempt to show a dependence of both the RMSE field and the mean RMSE on the square size. The importance of a suitable merger between the radar and rain gauge datasets is demonstrated by a comparison between the verification results obtained with and without the gauge adjustment. The application of verification procedure demonstrates uncertainties in the precipitation forecasts. The model was integrated with initial conditions shifted by 0.5° distances. The four verifications, corresponding to the shifts in the four directions, show differences in the resulting QPF, which depend on the size of verification area and on the direction of the shift.     

Climate change predictions for South America

Some of the characteristics of predicted climate changes for South America are analysed for the years 2010 and 2050. These predictions are based on the results of three-dimensional Global Circulation Models (GCMs). The results differ between models (GISS, NCAR-CCMs and GFDL), particularly when applied to regional and sub-regional scales and to time scales of less than one-year intervals. It is concluded that these differences are due to the particular structure of each model as well as to the lack of sufficient basic data from the South American sub-continent. The dynamics of vegetation cover play an important role in future water balance changes. The changes in surface temperatures predicted by the GISS model are discussed in relation to changes in the climatic-dynamic base stemming from anthropogenic changes in the vegetation cover.     

A design framework for creating social learning situations

Learning nurtured through experimentation is very important for enabling sustainability transitions. Over the last decade, different strands of research have investigated social learning and its associated processes to better understand learning efforts aimed at socio-technical system change. While some necessary process considerations to enable social learning have been established, actual design and organisation of experiments that aim to create a social learning situation remain largely unexplored. Against this background, this paper presents an empirical, mixed-method study that investigated a governance experiment within the Australian urban water sector. This experiment enabled widespread learning, resulting in socio-technical system change. The research reveals that social learning in particular is more complex in reality than in theory and that not all system stakeholders need to learn the same to achieve system change. Further, this paper develops a framework that outlines enabling starting conditions and features for designing and organising social learning situations. The framework comprises focus projects, multi-organisational peer groups, distributed facilitation, adaptability and flexibility, time and science/research. The key findings provide practical strategies for designing and operationalising policy and governance reform agendas that embrace learning situations.     

A framework for regional modeling of past climates

Summary The methods of reconstructing ancient climate information from the rock record are summarized, and the climate forcing factors that have been active at global and regional scales through Earth history are reviewed. In this context, the challenges and approaches to modeling past climates by using a regional climate model are discussed. A significant challenge to such modeling efforts arises if the time period of interest occurred prior to the past ∼3–5 million years, at which point land–sea distributions and topography markedly different from present must be specified at the spatial resolution required by regional climate models. Creating these boundary conditions requires a high degree of geologic knowledge, and also depends greatly upon the global climate model driving conditions. Despite this and other challenges, regional climate models represent an important and unique tool for paleoclimate investigations. Application of regional climate models to paleoclimate studies may provide another way to assess the overall performance of regional climate models.     

A GIS framework for Traffic Emission Information System

Summary A coefficient-based vehicular exhaust emission model is being developed jointly with the variable-grid urban airshed model (UAM-V) of Systems Applications International (SAI), and integrated within a GIS (ArcInfo) framework. When coupled with a road network, traffic features and meteorological as well as geographical databases, this framework produces a Traffic Emission Information System (TEIS). With this TEIS, the emission coefficients of CO, NOx and VOC for Sydney in the calendar year 2000 are derived; and the emission rates of the pollutants are then estimated using sample traffic profile data for the Sydney metropolitan area. Finally, ongoing improvements to the system are presented.     

A stochastic model of SST for climate simulation experiments

 This study describes the implementation of a statistical method to simulate a multi-century sequence of global sea surface temperature (SST) fields. A multi-variable auto-regressive (AR) model is trained on the observed time series of SST from the data set compiled at the Hadley Centre (GISST 2.0). To reduce the dimensionality of the model, the stochastic process is in practice fitted to empirical orthogonal function (EOF) time coefficients of the SST series, retaining the first 14 EOFs. Selected lag cross-covariances among the EOF time series are retained, based on the structure of the cross-correlation matrix and lags up to 64 months are included. Though the resulting system is quite large (a 14-dimensional AR process, with 400 parameters to be determined) the calculation is possible and a stable process is obtained. The process can then be used to investigate some statistical properties of the SST data set and to generate synthetic SST data that could be used in very long numerical experiments with atmospheric or ocean models in which only the main features of the observed statistics of the SST must be retained. Results indicate that the synthetic SST data set seems to be of usable quality as boundary condition for the atmosphere or the ocean in climate experiments. Analysis of extreme events and extreme decades in the synthetic SST data confirms the exceptional character of the 1980s, but also provides circumstantial evidence that the 1980s were indeed within the limits of the statistics of the previously observed record. Received: 6 August 1996 / Accepted: 29 September 1997     

Co-productive governance: A relational framework for adaptive governance

Adaptive governance focuses our attention on the relationships between science and management, whereby the so-called ‘gaps’ between these groups are seen to hinder effective adaptive responses to biophysical change. Yet the relationships between science and governance, knowledge and action, remain under theorized in discussions of adaptive governance, which largely focuses on abstract design principles or preferred institutional arrangements. In contrast, the metaphor of co-production highlights the social and political processes through which science, policy, and practice co-evolve. Co-production is invoked as a normative goal (Mitchell et al., 2004) and analytical lens (Jasanoff, 2004a, Jasanoff, 2004b), both of which provide useful insight into the processes underpinning adaptive governance. This paper builds on and integrates these disparate views to reconceptualize adaptive governance as a process of co-production. I outline an alternative conceptual framing, ‘co-productive governance’, that articulates the context, knowledge, process, and vision of governance. I explore these ideas through two cases of connectivity conservation, which draws on conservation science to promote collaborative cross-scale governance. This analysis highlights the ways in which the different contexts of these cases produced very different framings and responses to the same propositions of science and governance. Drawing on theoretical and empirical material, co-productive governance moves beyond long standing debates that institutions can be rationally crafted or must emerge from context resituate adaptive governance in a more critical and contextualized space. This reframing focuses on the process of governance through an explicit consideration of how normative considerations shape the interactions between knowledge and power, science and governance.     

Ecosystem stewardship: A resilience framework for arctic conservation

Ecosystem stewardship is a framework for actively shaping trajectories of ecological and social change to foster a more sustainable future for species, ecosystems, and society. We apply this framework to conservation challenges and opportunities in the Arctic, where the rapid pace of human-induced changes and their interactions force us now to consider a new relationship between people and the rest of nature. Biodiversity, which has traditionally been the target of conservation efforts, is increasingly affected by human impacts such as energy demand and industrial development that are motivated more by short-term profits than by concerns for societal consequences of long-term arctic biodiversity change. We posit that effective approaches to conservation must (a) foster both ecosystem resilience and human wellbeing, (b) integrate ecological and social processes across scales, and (c) take actions that shape the future rather than seeking only to restore the past. To this end, we identify progress through actions that have been or could be taken at local, national, and international scales to promote arctic resilience and conservation. A stewardship approach to conservation aims to prevent undesirable changes and prepares for adaptation to rapid and uncertain changes that cannot be avoided and for transformation to avoid or escape undesirable states. The greatest opportunity for arctic stewardship at the local scale may lie in building upon culturally engrained (often indigenous) respect for nature and reliance on local environment, empowering it through knowledge and power sharing with national regulatory frameworks. This, in turn, allows connection of drivers with impacts across scales and raises awareness of the value of human–environment relationships. At national and international scales stewardship provides rules for coordinated action to reconcile local and regional conservation actions with those that are motivated by constraints at the global level, to foster ecosystem integrity and human wellbeing in the face of transformative changes in environment, landscapes, species, and society.     

A conceptual framework for cross-border impacts of climate change

Climate change impacts, adaptation and vulnerability studies tend to confine their attention to impacts and responses within the same geographical region. However, this approach ignores cross-border climate change impacts that occur remotely from the location of their initial impact and that may severely disrupt societies and livelihoods. We propose a conceptual framework and accompanying nomenclature for describing and analysing such cross-border impacts. The conceptual framework distinguishes an initial impact that is caused by a climate trigger within a specific region. Downstream consequences of that impact propagate through an impact transmission system while adaptation responses to deal with the impact propagate through a response transmission system. A key to understanding cross-border impacts and responses is a recognition of different types of climate triggers, categories of cross-border impacts, the scales and dynamics of impact transmission, the targets and dynamics of responses and the socio-economic and environmental context that also encompasses factors and processes unrelated to climate change. These insights can then provide a basis for identifying relevant causal relationships. We apply the framework to the floods that affected industrial production in Thailand in 2011, and to projected Arctic sea ice decline, and demonstrate that the framework can usefully capture the complex system dynamics of cross-border climate impacts. It also provides a useful mechanism to identify and understand adaptation strategies and their potential consequences in the wider context of resilience planning. The cross-border dimensions of climate impacts could become increasingly important as climate changes intensify. We conclude that our framework will allow for these to be properly accounted for, help to identify new areas of empirical and model-based research and thereby support climate risk management.     

Modeling microclimate environments: A verification study

A numerical model is developed for simulating microclimate of plants and bare soil. The model evaluates heat, mass, momentum, and radiative fluxes in the soil-plant-atmosphere system. Its vertical domain may extend throughout the whole Planetary Boundary Layer (PBL). The model requires, either, temporal meteorological data of solar radiation, wind speed, air temperature and humidity measured over the field, or, when applied to the whole PBL, initial values of the latter three at its top. Vegetation parameters (leaf area index, photometric properties, root distribution and density) as well as soil texture, hydraulic and photometric properties are considered. The model was verified with meteorological data taken from two different climatological regions, above a bare soil and two cotton fields.For all case studies, observed and calculated values of air (except for within-canopy) and soil temperatures, wind speed, net radiation, and soil-, latent-, and sensible heat fluxes, agreed well with measurements.     

Preliminary evaluations of FGOALS-g2 for decadal predictions

The Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 (FGOALS-g2) for decadal predictions, is evaluated preliminarily, based on sets of ensemble 10-year hindcasts that it has produced. The results show that the hindcasts were more accurate in decadal variability of SST and surface air temperature (SAT), particularly in that of Nin o3.4 SST and China regional SAT, than the second sample of the historical runs for 20th-century climate (the control) by the same model. Both the control and the hindcasts represented the global warming well using the same external forcings, but the control overestimated the warming. The hindcasts produced the warming closer to the observations. Performance of FGOALS-g2 in hindcasts benefits from more realistic initial conditions provided by the initialization run and a smaller model bias resulting from the use of a dynamic bias correction scheme newly developed in this study. The initialization consists of a 61-year nudging-based assimilation cycle, which follows on the control run on 01 January 1945 with the incorporation of observation data of upper-ocean temperature and salinity at each integration step in the ocean component model, the LASG IAP Climate System Ocean Model, Version 2 (LICOM2). The dynamic bias correction is implemented at each step of LICOM2 during the hindcasts to reduce the systematic biases existing in upper-ocean temperature and salinity by incorporating multi-year monthly mean increments produced in the assimilation cycle. The effectiveness of the assimilation cycle and the role of the correction scheme were assessed prior to the hindcasts.     

A framework for nitrogen futures in the shared socioeconomic pathways

Humanity's transformation of the nitrogen cycle has major consequences for ecosystems, climate and human health, making it one of the key environmental issues of our time. Understanding how trends could evolve over the course of the 21^st century is crucial for scientists and decision-makers from local to global scales. Scenario analysis is the primary tool for doing so, and has been applied across all major environmental issues, including nitrogen pollution. However, to date most scenario efforts addressing nitrogen flows have either taken a narrow approach, focusing on a singular impact or sector, or have not been integrated within a broader scenario framework – a missed opportunity given the multiple environmental and socio-economic impacts that nitrogen pollution exacerbates. Capitalizing on our expanding knowledge of nitrogen flows, this study introduces a framework for new nitrogen-focused narratives based on the widely used Shared Socioeconomic Pathways that include all the major nitrogen-polluting sectors (agriculture, industry, transport and wastewater). These new narratives are the first to integrate the influence of climate and other environmental pollution control policies, while also incorporating explicit nitrogen-control measures. The next step is for them to be used as model inputs to evaluate the impact of different nitrogen production, consumption and loss trajectories, and thus advance understanding of how to address environmental impacts while simultaneously meeting key development goals. This effort is an important step in assessing how humanity can return to the planetary boundary of this essential element over the coming century.     

A framework for the structure of a low windspeed field

In this paper the structure of the wind field in near calm conditions is established. First, the main parameters that characterize the wind field are defined. By considering a bivariate Gaussian distribution for the components of the wind speed, which becomes a circular distribution in low wind speeds, simple relations among those parameters have been obtained. Some experimental evidence supporting the proposed formulation is given.     

A technique for verification of weather forecast and climate simulation with fuzzy sets

In the light of the idea of fuzzy neartude, a fuzzy procedure of verifying weather forecast and climate simula-tion is demonstrated. Several verification indices used in weather forecast, such as hit ratio, Heidke score, Brier score, correlation coefficient and information entropy score, are unified under fuzzy neartude and a new verifica-tion index is proposed on the basis of Hamming distance neartude. Further, D2-statistics used in climate sim-ulation verification is derived from a weighted Euclidean distance neartude. The “January climate” generated by general circulation model (GCM) is also numerically verified.     

Fundamental framework and experiments of the third generation of IAP / LASG world ocean general circulation model

A new generation of the IAP / LASG world ocean general circulation model is designed and presented based on the previous 20-layer model, with enhanced spatial resolutions and improved parameterizations. The model uses a triangular-truncated spectral horizontal grid system with its zonal wave number of 63 (T63) to match its atmospheric counterpart of a T63 spectral atmosphere general circulation model in a planned coupled ocean-atmosphere system. There are 30 layers in vertical direction, of which 20 layers are located above 1000 m for better depicting the permanent thermocline. As previous ocean models developed in IAP / LASG, a free surface (rather than “rigid-lid” approximation) is included in this model. Compared with the 20-layer model, some more detailed physical parameterizations are considered, including the along / cross isopycnal mixing scheme adapted from the Gent-MacWilliams scheme. The model is spun up from a motionless state. Initial conditions for temperature and salinity are taken from the three-dimensional distributions of Levitus’ annual mean observation. A preliminary analysis of the first 1000-year integration of a control experiment shows some encouraging improvements compared with the twenty-layer model, particularly in the simulations of permanent thermocline, thermohaline circu?lation, meridional heat transport, etc. resulted mainly from using the isopycnal mixing scheme. However, the use of isopycnal mixing scheme does not significantly improve the simulated equatorial thermocline. A series of numerical experiments show that the most important contribution to the improvement of equatori?al thermocline and the associated equatorial under current comes from reducing horizontal viscosity in the equatorial regions. It is found that reducing the horizontal viscosity in the equatorial Atlantic Ocean may slightly weaken the overturning rate of North Atlantic Deep Water.     

Multiple adaptation types with mitigation: A framework for policy analysis

Effective climate policy will consist of mitigation and adaptation implemented simultaneously in a policy portfolio to reduce the risks of climate change. Previous studies of the tradeoffs between mitigation and adaptation have implicitly framed the problem deterministically, choosing the optimal paths for all time. Because climate change is a long-term problem with significant uncertainties and opportunities to learn and revise, critical tradeoffs between mitigation and adaptation in the near-term have not been considered. We propose a new framework for considering the portfolio of mitigation and adaptation that explicitly treats the problem as a multi-stage decision under uncertainty. In this context, there are additional benefits to near-term investments if they reduce uncertainty and lead to improved future decisions. Two particular features are fundamental to understanding the relevant tradeoffs between mitigation and adaptation: (1) strategy dynamics over time in reducing climate damages, and (2) strategy dynamics under uncertainty and potential for learning. Our framework strengthens the argument for disaggregating adaption as has been proposed by others. We present three stylized classes of adaptation investment types as a conceptual framework: short-lived “flow” spending, committed “stock” investment, and lower capacity “option” stock with the capability of future upgrading. In the context of sequential decision under uncertainty, these subtypes of adaptation have important tradeoffs among them and with mitigation. We argue that given the large policy uncertainty that we face currently, explicitly considering adaptation “option” investments is a valuable component of a near-term policy response that can balance between the flexible flow and committed stock approaches, as it allows for the delay of costly stock investments while at the same time allowing for lower-cost risk management of future damages.     

Vulnerability: A generally applicable conceptual framework for climate change research

The term ‘vulnerability’ is used in many different ways by various scholarly communities. The resulting disagreement about the appropriate definition of vulnerability is a frequent cause for misunderstanding in interdisciplinary research on climate change and a challenge for attempts to develop formal models of vulnerability. Earlier attempts at reconciling the various conceptualizations of vulnerability were, at best, partly successful. This paper presents a generally applicable conceptual framework of vulnerability that combines a nomenclature of vulnerable situations and a terminology of vulnerability concepts based on the distinction of four fundamental groups of vulnerability factors. This conceptual framework is applied to characterize the vulnerability concepts employed by the main schools of vulnerability research and to review earlier attempts at classifying vulnerability concepts. None of these one-dimensional classification schemes reflects the diversity of vulnerability concepts identified in this review. The wide range of policy responses available to address the risks from global climate change suggests that climate impact, vulnerability, and adaptation assessments will continue to apply a variety of vulnerability concepts. The framework presented here provides the much-needed conceptual clarity and facilitates bridging the various approaches to researching vulnerability to climate change.