An approach to modeling climate based on feedback relationships

Feedback occurs between many components of the climate system, and makes the study of climate very difficult. A modeling approach is presented in which feedbacks are represented specifically. Analysis of very simple models shows how feedback between two components affects their behavior; positive feedback increases persistence, and can produce climatic changes even without changes in external forcing. In any quantitative study, the magnitudes of all relevant feedbacks must be known accurately. As an example, it is shown how the effect of CO₂ on global temperature must depend greatly on the feedback between global temperature and ice extent.     

High-resolution climate simulation of the last glacial maximum

The climate of the last glacial maximum (LGM) is simulated with a high-resolution atmospheric general circulation model, the NCAR CCM3 at spectral truncation of T170, corresponding to a grid cell size of roughly 75 km. The purpose of the study is to assess whether there are significant benefits from the higher resolution simulation compared to the lower resolution simulation associated with the role of topography. The LGM simulations were forced with modified CLIMAP sea ice distribution and sea surface temperatures (SST) reduced by 1°C, ice sheet topography, reduced CO₂, and 21,000 BP orbital parameters. The high-resolution model captures modern climate reasonably well, in particular the distribution of heavy precipitation in the tropical Pacific. For the ice age case, surface temperature simulated by the high-resolution model agrees better with those of proxy estimates than does the low-resolution model. Despite the fact that tropical SSTs were only 2.1°C less than the control run, there are many lowland tropical land areas 4–6°C colder than present. Comparison of T170 model results with the best constrained proxy temperature estimates (noble gas concentrations in groundwater) now yield no significant differences between model and observations. There are also significant upland temperature changes in the best resolved tropical mountain belt (the Andes). We provisionally attribute this result in part as resulting from decreased lateral mixing between ocean and land in a model with more model grid cells. A longstanding model-data discrepancy therefore appears to be resolved without invoking any unusual model physics. The response of the Asian summer monsoon can also be more clearly linked to local geography in the high-resolution model than in the low-resolution model; this distinction should enable more confident validation of climate proxy data with the high-resolution model. Elsewhere, an inferred salinity increase in the subtropical North Atlantic may have significant implications for ocean circulation changes during the LGM. A large part of the Amazon and Congo Basins are simulated to be substantially drier in the ice age—consistent with many (but not all) paleo data. These results suggest that there are considerable benefits derived from high-resolution model regarding regional climate responses, and that observationalists can now compare their results with models that resolve geography at a resolution comparable to that which the proxy data represent.     

Validation and Application of Plants as Biomonitors of Trace Element Atmospheric Pollution – A Co-Ordinated Effort in 14 Countries

The International Atomic Energy Agency (IAEA) organized a co-ordinated research project (CRP) on Validation and application of plants as biomonitors of trace element atmospheric pollution analysed by nuclear and related techniques involving 14 participating countries. The CRPs objective was to identify appropriate bioindicators for local and/or regional application and validate them for general air pollution monitoring. Activities included quantification studies, research into spatial and time resolution for particular organisms, and physiological studies. A number of suitable bioindicators were identified in different parts of the globe and tested during the CRP. Sampling strategies were reviewed and the recommended approach adopted by the group. Appropriate sample preparation procedures were assessed and harmonised to the degree allowed by different geographic and climatic conditions in the participating countries. Two interlaboratory comparison exercises were carried out on lichen and moss materials. Results confirmed definite improvement in analytical performance of the participating laboratories, but also revealed possible inconsistencies due to different sample processing procedures. Several monitoring surveys were carried out and consequently pollution maps drawn for extended areas or countries. Overall results confirmed applicability of lower plants for assessing the degree of atmospheric pollution and provided several countries with effective monitoring tools not used before.     

Evaluation of turbulent fluxes over Maitri,Antarctica

Turbulent fluxes have been evaluated for clear sunny days over the Indian Antarctic station, Maitri, using the basic meteorological data recorded at four levels of a 28 m tower. The data are supplemented with radiation data. The surface layer over Maitri remains thermally stratified during the hours of minimum solar insolation, the so-called nighttime period. The surface winds during this period are generally very strong resulting in high momentum fluxes. In particular, for high winds (>12 m s^–1), the temperature gradient is found to be less positive than for moderate winds (4 to 7 m s^–1). Solar insolation provided the daytime heating necessary for the diurnal variation of atmospheric stability, and hence, for the turbulent fluxes. Thus, on clear days daytime conditions are marked by upward transport of heat with reduced momentum flux, while stable nighttime conditions are marked by a downward heat flux with increased momentum fluxes.     

Vertical behaviour and meteorological properties of air masses in the southwest of the Iberian Peninsula (1997–2007)

Air masses are characterized by physical (temperature, humidity) and chemical (transported gases and aerosols) properties, being associated their arrival to different meteorological scenarios. The knowledge of the air masses over a region is fundamental as complementary information in several atmospheric studies, being the calculation of back-trajectory the most widely used tool whenever air masses are analyzed. A study of air masses has been carried out in southwestern Iberian Peninsula using 5-day kinematic back trajectories computed by the HYSPLIT model at three heights (500, 1,500 and 3,000 m) from 1997 to 2007. The main aims have been to characterize their vertical behaviour and their thermal and humidity properties. Thirteen trajectory clusters have been defined, showing the northerly and westerly clusters a high coupling degree. Seasonal daily variation of potential temperature and specific humidity has been analyzed, obtaining higher differences among clusters in the cold season.     

Air Pollution Transport in an Alpine Valley: Results From Airborne and Ground-Based Observations

An observational dataset from a wintertime field campaign in the Inn Valley, Austria, is analysed in order to study mechanisms of air pollution transport in an Alpine valley. The results illustrate three types of mechanisms: transport by a density current, back-and-forth transport by valley winds, and transport by slope winds. The first type is associated with an air mass difference along the valley. Cooler air located in the lower part of the valley behaves like a density current and produces the advection of pollutants by upvalley winds. In the second type, strong horizontal gradients in pollution concentrations exist close to ground. Multiple wind reversals result in a back-and-forth transport of pollutants by weak valley winds. In the third type, upslope winds during daytime decrease low-level pollution concentrations and cause the formation of elevated pollution layers.     

A verification framework for interannual-to-decadal predictions experiments

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.     

An empirical framework for tropical cyclone climatology

An empirical approach for analyzing tropical cyclone climate is presented. The approach uses lifetime-maximum wind speed and cyclone frequency to induce two orthogonal variables labeled “activity” and “efficiency of intensity”. The paired variations of activity and efficiency of intensity along with the opponent variations of frequency and intensity configure a framework for evaluating tropical cyclone climate. Although cyclone activity as defined in this framework is highly correlated with the commonly used exponent indices like accumulated cyclone energy, it does not contain cyclone duration. Empirical quantiles are used to determine threshold intensity levels, and variant year ranges are used to find consistent trends in tropical cyclone climatology. In the western North Pacific, cyclone activity is decreasing despite increases in lifetime-maximum intensity. This is due to overwhelming decreases in cyclone frequency. These changes are also explained by an increasing efficiency of intensity. The North Atlantic shows different behavior. Cyclone activity is increasing due to increasing frequency and, to a lesser extent, increasing intensity. These changes are also explained by a decreasing efficiency of intensity. Tropical cyclone trends over the North Atlantic basin are more consistent over different year ranges than tropical cyclone trends over the western North Pacific.     

Bottom-up climate risk assessment of infrastructure investment in the Niger River Basin

The Niger River is the third largest river in the African continent. Nine riparian countries share its basin, which rank all among the world’s thirty poorest. Existing challenges in West Africa, including endemic poverty, inadequate infrastructure and weak adaptive capacity to climate variability, make the region vulnerable to climate change. In this study, a risk-based methodology is introduced and demonstrated for the analysis of climate change impacts on planned infrastructure investments in water resources systems in the Upper and Middle Niger River Basin. The methodology focuses on identifying the vulnerability of the Basin’s socio-economic system to climate change, and subsequently assessing the likelihood of climate risks by using climate information from a multi-run, multi-GCM ensemble of climate projections. System vulnerabilities are analyzed in terms of performance metrics of hydroelectricity production, navigation, dry and rainy season irrigated agriculture, flooding in the Inner Delta of the Niger and the sustenance of environmental flows. The study reveals low to moderate risks in terms of stakeholder-defined threshold levels for most metrics in the 21st Century. The highest risk levels were observed for environmental flow targets. The findings indicate that the range of projected changes in an ensemble of CMIP3 GCM projections imply only relatively low risks of unacceptable climate change impacts on the present large-scale infrastructure investment plan for the Basin.     

A climate change simulation starting from 1935

Due to restrictions in the available computing resources and a lack of suitable observational data, transient climate change experiments with global coupled ocean-atmosphere models have been started from an initial state at equilibrium with the present day forcing. The historical development of greenhouse gas forcing from the onset of industrialization until the present has therefore been neglected. Studies with simplified models have shown that this cold start error leads to a serious underestimation of the anthropogenic global warming. In the present study, a 150-year integration has been carried out with a global coupled ocean-atmosphere model starting from the greenhouse gas concentration observed in 1935, i.e., at an early time of industrialization. The model was forced with observed greenhouse gas concentrations up to 1985, and with the equivalent C0₂ concentrations stipulated in Scenario A (Business as Usual) of the Intergovernmental Panel on Climate Change from 1985 to 2085. The early starting date alleviates some of the cold start problems. The global mean near surface temperature change in 2085 is about 0.3 K (ca. 10%) higher in the early industrialization experiment than in an integration with the same model and identical Scenario A greenhouse gas forcing, but with a start date in 1985. Comparisons between the experiments with early and late start dates show considerable differences in the amplitude of the regional climate change patterns, particularly for sea level. The early industrialization experiment can be used to obtain a first estimate of the detection time for a greenhouse-gas-induced near-surface temperature signal. Detection time estimates are obtained using globally and zonally averaged data from the experiment and a long control run, as well as principal component time series describing the evolution of the dominant signal and noise modes. The latter approach yields the earliest detection time (in the decade 1990–2000) for the time-evolving near-surface temperature signal. For global-mean temperatures or for temperatures averaged between 45°N and 45°S, the signal detection times are in the decades 2015–2025 and 2005–2015, respectively. The reduction of the cold start error in the early industrialization experiment makes it possible to separate the near-surface temperature signal from the noise about one decade earlier than in the experiment starting in 1985. We stress that these detection times are only valid in the context of the coupled model's internally-generated natural variability, which possibly underestimates low frequency fluctuations and does not incorporate the variance associated with changes in external forcing factors, such as anthropogenic sulfate aerosols, solar variability or volcanic dust.