Daily Meteorological Observations in Cádiz – San Fernando. Analysis of the Documentary Sources and the Instrumental Data Content (1786–1996)

Meteorological observations in the city of Cádiz are acknowledged as having been made from the middle of the 18th century onwards although they were only recorded and preserved in documentary form from 1789 onwards. Data readings were taken at the new Naval Observatory in San Fernando, ten kilometres from Cádiz, from 1797 onwards. Continuous series for temperature and atmospheric pressure at a daily resolution have been compiled and constructed from 1817 until 1996. The data series is composed of thrice daily observations made at Cádiz (1821–1880) by local observers and hourly data from Naval Observatory at San Fernando (1870–1996).     

Climate change adaptation of coffee production in space and time

Savannas constitute the most fire-prone vegetation type on earth and are a significant source of greenhouse gas emissions. Most savanna fires are lit by people for a variety of livelihood applications. ‘Savanna burning’ is an accountable activity under the Kyoto Protocol, but only Australia, as a developed economy, accounts for emissions from this source in its national accounts. Over the past decade considerable effort has been given to developing savanna burning projects in northern Australia, combining customary indigenous (Aboriginal) approaches to landscape-scale fire management with development of scientifically robust emissions accounting methodologies. Formal acceptance by the Australian Government of that methodology, and its inclusion in Australia’s developing emissions trading scheme, paves the way for Aboriginal people to commercially benefit from savanna burning projects. The paper first describes this Australian experience, and then explores options for implementing community-based savanna burning emissions reduction projects in other continental savanna settings, specifically in Namibia and Venezuela. These latter examples illustrate that savanna fire management approaches potentially have broader application for contributing to livelihood opportunities in other fire-prone savanna regions.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.     

Carbon pricing in practice: a review of existing emissions trading systems

This article analyses the implementation of emissions trading systems (ETSs) in eight jurisdictions: the EU, Switzerland, the Regional Greenhouse Gas Initiative (RGGI) and California in the US, Québec in Canada, New Zealand, the Republic of Korea and pilot schemes in China. The article clarifies what is working, what isn’t and why, when it comes to the practice of implementing an ETS. The eight ETSs are evaluated against five main criteria: environmental effectiveness, economic efficiency, market management, revenue management and stakeholder engagement. Within each of these categories, ETS attributes ? including abatement cost, stringency of the cap, improved allocation practices over time and the trajectory of price stability ? are assessed for each system. Institutional learning, administrative prudence, appropriate carbon revenue management and stakeholder engagement are identified as key ingredients for successful ETS regimes. Recent implementation of ETSs in regions including California, Québec and South Korea indicates significant institutional learning from prior systems, especially the EU ETS, with these regions implementing more robust administrative and regulatory structures suitable for handling unique national and sub-national opportunities and constraints. The analysis also shows that there is potential for a ‘double dividend’ in emissions reductions even with a modest carbon price, provided the cap tightens over time and a portion of the auctioned revenues are reinvested in other emissions-reduction activities. Knowledge gaps exist in understanding the interaction of pricing instruments with other climate policy instruments and how governments manage these policies to achieve optimum emissions reductions with lower administrative costs.

Key policy insights

• Countries are learning from each other on ETS implementation.

• Administrative and regulatory structures of ETS jurisdictions appear to evolve and become more robust in every ETS analysed.

• A ‘double dividend’ for emissions reductions may also exist in cases where mitigation occurs as a result of the ETS policy and when auction revenues are reinvested in other emissions-reduction activities.

     

Projections of severe droughts in future climate in Southeast Brazil: a case study in Southern Minas Gerais State,Brazil

Theoretical and Applied Climatology - South of Minas Gerais state, in Southeast Brazil, is known for the coffee crop production (more than 30% of country’s production) and hydroelectricity...     

Ocean–atmosphere coupling and the boreal winter MJO

The influence of ocean–atmosphere coupling on the simulation and prediction of the boreal winter Madden–Julian Oscillation (MJO) is examined using the Seoul National University coupled general circulation model (CGCM) and atmospheric—only model (AGCM). The AGCM is forced with daily SSTs interpolated from pentad mean CGCM SSTs. Forecast skill is examined using serial extended simulations spanning 26 different winter seasons with 30-day forecasts commencing every 5 days providing a total of 598 30-day simulations. By comparing both sets of experiments, which share the same atmospheric components, the influence of coupled ocean–atmosphere processes on the simulation and prediction of MJO can be studied. The mean MJO intensity possesses more realistic amplitude in the CGCM than in AGCM. In general, the ocean–atmosphere coupling acts to improve the simulation of the spatio-temporal evolution of the eastward propagating MJO and the phase relationship between convection (OLR) and SST over the equatorial Indian Ocean and the western Pacific. Both the CGCM and observations exhibit a near-quadrature relationship between OLR and SST, with the former lagging by about two pentads. However, the AGCM shows a less realistic phase relationship. As the initial conditions are the same in both models, the additional forcing by SST anomalies in the CGCM extends the prediction skill beyond that of the AGCM. To test the applicability of the CGCM to real-time prediction, we compute the Real-time Multivariate MJO (RMM) index and compared it with the index computed from observations. RMM1 (RMM2) falls away rapidly to 0.5 after 17–18 (15–16) days in the AGCM and 18–19 (16–17) days in the CGCM. The prediction skill is phase dependent in both the CGCM and AGCM.     

Extreme climatic events in the Amazon basin

During 2009 the Amazon basin was hit by a heavy flooding with a magnitude and duration few times observed in several decades. Torrential rain in northern and eastern Amazonia during the austral summer of 2008–2009 swelled the Amazon River and its tributaries. By July 2009, water levels of the Rio Negro, a major Amazon tributary, reached at Manaus harbor a new record, the highest mark of the last 107?years. During the 2008–2009 hydrological year, the rainy season on northern and northwestern Amazonia started prematurely, and was followed by a longer-than-normal rainy season. An anomalously southward migration of the ITCZ during May–June 2009, due to the warmer than normal surface waters in the tropical South Atlantic, was responsible for abundant rainfall in large regions of eastern Amazonia and Northeast Brazil from May to July 2009. We also compared the flood of 2009 with other major events recorded in 1989 and 1999. The hydrological consequences of this pattern were earlier than normal floods in Amazon northern tributaries, which peak discharges at their confluences with the main stem almost coincided with the peaks of southern tributaries. Since the time displacement of the contribution to the main stem of northern and southern Amazon tributaries is fundamental for damping flood waves in the main stem, the simultaneous combinations of peak discharges of tributaries resulted in an extreme flood.     

Evolution and modulation of tropical heating from the last glacial maximum through the twenty-first century

Twentieth century observations show that during the last 50?years the sea-surface temperature (SST) of the tropical oceans has increased by ~0.5°C and the area of SST >26.5 and 28°C (arbitrarily referred to as the oceanic warm pool: OWP) by 15 and 50% respectively in association with an increase in green house gas concentrations, with non-understood natural variability or a combination of both. Based on CMIP3 projections the OWP is projected to double during twenty-first century in a moderate CO₂ forcing scenario (IPCC A1B scenario). However, during the observational period the area of positive atmospheric heating (referred to as the dynamic warm pool, DWP), has remained constant. The threshold SST (T _H ), which demarks the region of net heating and cooling, has increased from 26.6°C in the 1950s to 27.1°C in the last decade and it is projected to increase to ~28.5°C by 2100. Based on climate model simulations, the area of the DWP is projected to remain constant during the twenty-first century. Analysis of the paleoclimate model intercomparison project (PMIP I and II) simulations for the Last Glacial maximum and the Mid-Holocene periods show a very similar behaviour, with a larger OWP in periods of elevated tropical SST, and an almost constant DWP associated with a varying T _H . The constancy of the DWP area, despite shifts in the background SST, is shown to be the result of a near exact matching between increases in the integrated convective heating within the DWP and the integrated radiative cooling outside the DWP as SST changes. Although the area of the DWP remains constant, the total tropical atmospheric heating is a strong function of the SST. For example the net heating has increased by about 10% from 1950 to 2000 and it is projected to increase by a further 20% by 2100. Such changes must be compensated by a more vigorous atmospheric circulation, with growth in convective heating within the warm pool, and an increase of subsiding air and stability outside the convective warm pool and an increase of vertical shear at the DWP boundaries. This finding is contrary to some conclusions from other studies but in accord with others. We discuss the similarities and differences at length.     

Possible climatic impacts of tropical deforestation

Large-scale conversion of tropical forests into pastures or annual crops will likely lead to changes in the local microclimate of those regions. Larger diurnal fluctuations of surface temperature and humidity deficit, increased surface runoff during rainy periods and decreased runoff during the dry season, and decreased soil moistrue are to be expected.It is likely that evapotranspiration will be reduced because of less available radiative energy at the canopy level since grass presents a higher albedo than forests, also because of the reduced availability of soil moisture at the rooting zone primarily during the dry season. Recent results from general circulation model (GCM) simulations of Amazonian deforestation seem to suggest that the equilibrium climate for a grassy vegetation in Amazonia would be one in which regional precipitation would be significantly reduced.Global climate changes probably will occur if there is a marked change in rainfall patterns in tropical forest regions as a result of deforestation. Besides that, biomass burning of tropical forests is likely adding CO₂ into the atmosphere, thus contributing to the enhanced greenhouse warming.     

Objective probabilities about future climate are a matter of opinion

In this paper, the unfeasibility of producing “objective” probabilistic climate change scenarios is discussed. Realizing that the knowledge of “true” probabilities of the different scenarios and temperature changes is unachievable, the objective must be to find the probabilities that are the most consistent with what our state of knowledge and expert judgment are. Therefore, subjective information plays, and should play, a crucial role. A new methodology, based on the Principle of Maximum Entropy, is proposed for constructing probabilistic climate change scenarios when only partial information is available. The objective is to produce relevant information for decision-making according to different agents’ judgment and subjective beliefs. These estimates have desirable properties such as: they are the least biased estimate possible on the available information; maximize the uncertainty (entropy) subject to the partial information that is given; The maximum entropy distribution assigns a positive probability to every event that is not excluded by the given information; no possibility is ignored. The probabilities obtained in this manner are the best predictions possible with the state of knowledge and subjective information that is available. This methodology allows distinguishing between reckless and cautious positions regarding the climate change threat.