Foreword: International Space Science Institute (ISSI) workshop on the Earth’s cryosphere and sea level change

Rising sea level is perhaps the most severe consequence of climate warming, as much of the world’s population and infrastructure is located near current sea level (Lemke et al. 2007). A major rise of a metre or more would cause serious problems. Such possibilities have been suggested by Hansen and Sato (2011) who pointed out that sea level was several metres higher than now during the Holsteinian and Eemian interglacials (about 250,000 and 120,000 years ago, respectively), even though the global temperature was then only slightly higher than it is nowadays. It is consequently of the utmost importance to determine whether such a sea level rise could occur and, if so, how fast it might happen. Sea level undergoes considerable changes due to natural processes such as the wind, ocean currents and tidal motions. On longer time scales, the sea level is influenced by steric effects (sea water expansion caused by temperature and salinity changes of the ocean) and by eustatic effects caused by changes in ocean mass. Changes in the Earth’s cryosphere, such as the retreat or expansion of glaciers and land ice areas, have been the dominant cause of sea level change during the Earth’s recent history. During the glacial cycles of the last million years, the sea level varied by a large amount, of the order of 100 m. If the Earth’s cryosphere were to disappear completely, the sea level would rise by some 65 m. The scientific papers in the present volume address the different aspects of the Earth’s cryosphere and how the different changes in the cryosphere affect sea level change. It represents the outcome of the first workshop held within the new ISSI Earth Science Programme. The workshop took place from 22 to 26 March, 2010, in Bern, Switzerland, with the objective of providing an in-depth insight into the future of mountain glaciers and the large land ice areas of Antarctica and Greenland, which are exposed to natural and anthropogenic climate influences, and their effects on sea level change. The participants of the workshop are experts in different fields including meteorology, climatology, oceanography, glaciology and geodesy; they use advanced space-based observational studies and state-of-the-art numerical modelling.

Comparison of the open-closed separatrix in a global magnetospheric simulation with observations: the role of the ring current

The development of global magnetospheric models, such as Space Weather Modeling Framework (SWMF), which can accurately reproduce and track space weather processes has high practical utility. We present an interval on 5 June 1998, where the location of the polar cap boundary, or open-closed field line boundary (OCB), can be determined in the ionosphere using a combination of instruments during a period encompassing a sharp northward to southward interplanetary field turning. We present both point- and time-varying comparisons of the observed and simulated boundaries in the ionosphere and find that when using solely the coupled ideal magnetohydrodynamic magnetosphere-ionosphere model, the rate of change of the OCB to a southward turning of the interplanetary field is significantly faster than that computed from the observational data. However, when the inner magnetospheric module is incorporated, the modeling framework both qualitatively, and often quantitatively, reproduces many elements of the studied interval prior to an observed substorm onset. This result demonstrates that the physics of the inner magnetosphere is critical in shaping the boundary between open and closed field lines during periods of southward interplanetary magnetic field (IMF) and provides significant insight into the 3-D time-dependent behavior of the Earth's magnetosphere in response to a northward-southward IMF turning. We assert that during periods that do not include the tens of minutes surrounding substorm expansion phase onset, the coupled SWMF model may provide a valuable and reliable tool for estimating both the OCB and magnetic field topology over a wide range of latitudes and local times.

Optimising the FAMOUS climate model: inclusion of global carbon cycling

FAMOUS fills an important role in the hierarchy of climate models, both explicitly resolving atmospheric and oceanic dynamics yet being sufficiently computationally efficient that either very long simulations or large ensembles are possible. An improved set of carbon cycle parameters for this model has been found using a perturbed physics ensemble technique. This is an important step towards building the "Earth System" modelling capability of FAMOUS, which is a reduced resolution, and hence faster running, version of the Hadley Centre Climate model, HadCM3. Two separate 100 member perturbed parameter ensembles were performed; one for the land surface and one for the ocean. The land surface scheme was tested against present-day and past representations of vegetation and the ocean ensemble was tested against observations of nitrate. An advantage of using a relatively fast climate model is that a large number of simulations can be run and hence the model parameter space (a large source of climate model uncertainty) can be more thoroughly sampled. This has the associated benefit of being able to assess the sensitivity of model results to changes in each parameter. The climatologies of surface and tropospheric air temperature and precipitation are improved relative to previous versions of FAMOUS. The improved representation of upper atmosphere temperatures is driven by improved ozone concentrations near the tropopause and better upper level winds.

Terrestrial ecosystems from space: a review of earth observation products for macroecology applications

Aim Earth observation (EO) products are a valuable alternative to spectral vegetation indices. We discuss the availability of EO products for analysing patterns in macroecology, particularly related to vegetation, on a range of spatial and temporal scales. Location Global. Methods We discuss four groups of EO products: land cover/cover change, vegetation structure and ecosystem productivity, fire detection, and digital elevation models. We address important practical issues arising from their use, such as assumptions underlying product generation, product accuracy and product transferability between spatial scales. We investigate the potential of EO products for analysing terrestrial ecosystems. Results Land cover, productivity and fire products are generated from long-term data using standardized algorithms to improve reliability in detecting change of land surfaces. Their global coverage renders them useful for macroecology. Their spatial resolution (e.g. GLOBCOVER vegetation, 300 m; MODIS vegetation and fire, ≥ 500 m; ASTER digital elevation, 30 m) can be a limiting factor. Canopy structure and productivity products are based on physical approaches and thus are independent of biome-specific calibrations. Active fire locations are provided in near-real time, while burnt area products show actual area burnt by fire. EO products can be assimilated into ecosystem models, and their validation information can be employed to calculate uncertainties during subsequent modelling. Main conclusions Owing to their global coverage and long-term continuity, EO end products can significantly advance the field of macroecology. EO products allow analyses of spatial biodiversity, seasonal dynamics of biomass and productivity, and consequences of disturbances on regional to global scales. Remaining drawbacks include inter-operability between products from different sensors and accuracy issues due to differences between assumptions and models underlying the generation of different EO products. Our review explains the nature of EO products and how they relate to particular ecological variables across scales to encourage their wider use in ecological applications.

On climate change and cultural geography: farming on the Lizard Peninsula, Cornwall, UK

Climate change as a global problem has moved relatively swiftly into high profile political debates over the last 20 years or so, with a concomitant diffusion from the natural sciences into the social sciences. The study of the human dimensions of climate change has been growing in momentum through research which attempts to describe, evaluate, quantify and model perceptions of climate change, understand more about risk and assess the construction of policy. Cultural geographers’ concerns with the construction of knowledge, the workings of social relations in space and the politics and poetics of place-based identities provide a lens through which personal, collective and institutional responses to climate change can be evaluated using critical and interpretative methodologies. Adopting a cultural geography approach, this paper examines how climate change as a particular environmental discourse is constructed through memory, observation and conversation, as well as materialised in farming practices on the Lizard Peninsula, Cornwall, UK

Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP)

Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH4). Increased wetland CH4 emissions could act as a positive feedback to future warming. The Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large-scale wetland characteristics and corresponding CH4 emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO2) forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO2 concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prognostically, while other models relied on remotely sensed inundation datasets, or an approach intermediate between the two. Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH4 emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH4 emissions, but large variation between the models remains. For annual global CH4 emissions, the models vary by ±40% of the all-model mean (190 Tg CH4 yr−1). Second, all models show a strong positive response to increased atmospheric CO2 concentrations (857 ppm) in both CH4 emissions and wetland area. In response to increasing global temperatures (+3.4 °C globally spatially uniform), on average, the models decreased wetland area and CH4 fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH4 fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatial scale comparable to model grid cells (commonly 0.5°). This limitation severely restricts our ability to model global wetland CH4 emissions with confidence. Our simulated wetland extents are also difficult to evaluate due to extensive disagreements between wetland mapping and remotely sensed inundation datasets. Fourth, the large range in predicted CH4 emission rates leads to the conclusion that there is both substantial parameter and structural uncertainty in large-scale CH4 emission models, even after uncertainties in wetland areas are accounted for.

Global snow mass measurements and the effect of stratigraphic detail on inversion of microwave brightness temperatures

Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as Snow Water Equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment (NASA CLPX) and the Helsinki University of Technology (HUT) microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 GHz and 37 GHz vertically polarised microwaves are consistent with Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Special Sensor Microwave Imager (SSM/I) retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10 cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method then it is equivalent to ±13 mm SWE (7% of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model.

How well can we simulate past climates? Evaluating the models using global palaeoenvironmental datasets.

Global syntheses of palaeoenvironmental data are required to test climate models under conditions different from the present. Data sets for this purpose contain data from spatially extensive networks of sites. The data are either directly comparable to model output or readily interpretable in terms of modelled climate variables. Data sets must contain sufficient documentation to distinguish between raw (primary) and interpreted (secondary, tertiary) data, to evaluate the assumptions involved in interpretation of the data, to exercise quality control, and to select data appropriate for specific goals. Four data bases for the Late Quaternary, documenting changes in lake levels since 30 kyr BP (the Global Lake Status Data Base), vegetation distribution at 18 kyr and 6 kyr BP (BIOME 6000), aeolian accumulation rates during the last glacial-interglacial cycle (DIRTMAP), and tropical terrestrial climates at the Last Glacial Maximum (the LGM Tropical Terrestrial Data Synthesis) are summarised. Each has been used to evaluate simulations of Last Glacial Maximum (LGM: 21 calendar kyr BP) and/or mid-Holocene (6 cal. kyr BP) environments. Comparisons have demonstrated that changes in radiative forcing and orography due to orbital and ice-sheet variations explain the first-order, broad-scale (in space and time) features of global climate change since the LGM. However, atmospheric models forced by 6 cal. kyr BP orbital changes with unchanged surface conditions fail to capture quantitative aspects of the observed climate, including the greatly increased magnitude and northward shift of the African monsoon during the early to mid-Holocene. Similarly, comparisons with palaeoenvironmental datasets show that atmospheric models have underestimated the magnitude of cooling and drying of much of the land surface at the LGM. The inclusion of feedbacks due to changes in ocean- and land-surface conditions at both times, and atmospheric dust loading at the LGM, appears to be required in order to produce a better simulation of these past climates. The development of Earth system models incorporating the dynamic interactions among ocean, atmosphere, and vegetation is therefore mandated by Quaternary science results as well as climatological principles. For greatest scientific benefit, this development must be paralleled by continued advances in palaeodata analysis and synthesis, which in turn will help to define questions that call for new focused data collection efforts.

How well can we measure the ocean’s mean dynamic topography from space?

Recent gravity missions have produced a dramatic improvement in our ability to measure the ocean’s mean dynamic topography (MDT) from space. To fully exploit this oceanic observation, however, we must quantify its error. To establish a baseline, we first assess the error budget for an MDT calculated using a 3rd generation GOCE geoid and the CLS01 mean sea surface (MSS). With these products, we can resolve MDT spatial scales down to 250 km with an accuracy of 1.7 cm, with the MSS and geoid making similar contributions to the total error. For spatial scales within the range 133–250 km the error is 3.0 cm, with the geoid making the greatest contribution. For the smallest resolvable spatial scales (80–133 km) the total error is 16.4 cm, with geoid error accounting for almost all of this. Relative to this baseline, the most recent versions of the geoid and MSS fields reduce the long and short-wavelength errors by 0.9 and 3.2 cm, respectively, but they have little impact in the medium-wavelength band. The newer MSS is responsible for most of the long-wavelength improvement, while for the short-wavelength component it is the geoid. We find that while the formal geoid errors have reasonable global mean values they fail capture the regional variations in error magnitude, which depend on the steepness of the sea floor topography.

Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI)

Sea surface temperature (SST) datasets have been generated from satellite observations for the period 1991–2010, intended for use in climate science applications. Attributes of the datasets specifically relevant to climate applications are: first, independence from in situ observations; second, effort to ensure homogeneity and stability through the time-series; third, context-specific uncertainty estimates attached to each SST value; and, fourth, provision of estimates of both skin SST (the fundamental measure- ment, relevant to air-sea fluxes) and SST at standard depth and local time (partly model mediated, enabling comparison with his- torical in situ datasets). These attributes in part reflect requirements solicited from climate data users prior to and during the project. Datasets consisting of SSTs on satellite swaths are derived from the Along-Track Scanning Radiometers (ATSRs) and Advanced Very High Resolution Radiometers (AVHRRs). These are then used as sole SST inputs to a daily, spatially complete, analysis SST product, with a latitude-longitude resolution of 0.05°C and good discrimination of ocean surface thermal features. A product user guide is available, linking to reports describing the datasets’ algorithmic basis, validation results, format, uncer- tainty information and experimental use in trial climate applications. Future versions of the datasets will span at least 1982–2015, better addressing the need in many climate applications for stable records of global SST that are at least 30 years in length.

The first real-time worldwide ionospheric predictions network: an advance in support of spaceborne experimentation, on-line model validation, and space weather

We report on the first realtime ionospheric predictions network and its capabilities to ingest a global database and forecast F-layer characteristics and "in situ" electron densities along the track of an orbiting spacecraft. A global network of ionosonde stations reported around-the-clock observations of F-region heights and densities, and an on-line library of models provided forecasting capabilities. Each model was tested against the incoming data; relative accuracies were intercompared to determine the best overall fit to the prevailing conditions; and the best-fit model was used to predict ionospheric conditions on an orbit-to-orbit basis for the 12-hour period following a twice-daily model test and validation procedure. It was found that the best-fit model often provided averaged (i.e., climatologically-based) accuracies better than 5% in predicting the heights and critical frequencies of the F-region peaks in the latitudinal domain of the TSS-1R flight path. There was a sharp contrast however, in model-measurement comparisons involving predictions of actual, unaveraged, along-track densities at the 295 km orbital altitude of TSS-1R In this case, extrema in the first-principle models varied by as much as an order of magnitude in density predictions, and the best-fit models were found to disagree with the "in situ" observations of Ne by as much as 140%. The discrepancies are interpreted as a manifestation of difficulties in accurately and self-consistently modeling the external controls of solar and magnetospheric inputs and the spatial and temporal variabilities in electric fields, thermospheric winds, plasmaspheric fluxes, and chemistry.