This paper analyzed the spatio-temporal variability of droughts in Cali, Colombia and their primary relations to the El Niño Southern Oscillation (ENSO). The Standardized Precipitation Index (SPI) was used to detect drought events from monthly rainfall data of 24 stations well spread over Cali during 1971-2011. The SPI provided the drought intensity, magnitude, frequency, and the minimum rainfall thresholds, mainly on an annual scale (SPI-12). Eighty percent of the stations reported four events with dry conditions in Cali: 1976-1977, 1983-1984, 1990-1992 and 2009-2010. The ENSO influence was evaluated using the correlation and wavelet transform analyses. Significant (non-significant) negative correlations between SPI-12 in the northern (southern) part of Cali, the multivariate ENSO Index (MEI), and Sea Surface Temperature (SST) Niño 3.4 and Niño 4 indices were observed. The wavelet coherence analysis showed significant coherencies between ENSO and SPI-12: at interannual scale (4-6 years), the phase difference of –135º generates a lag of 6-9 months between the minimum peak of the SPI-12 and the maximum peak of the indices. On the quasi-biennial scale (2-3 years), the phase difference of –180º suggests that the maximum wet (dry) conditions coincide with the mature stage of the La Niña (El Niño) event; and on the decadal scale (8-16 years), the decreases (increases) in rainfall precede the El Niño (La Niña) mature stage by approximately 10-18 months. These results are relevant for seasonal forecasting, since changes in SST in the equatorial Pacific may take place 6-18 months ahead of the dry conditions in Cali.

In Mexico, intense rains generated by tropical cyclones, cold fronts, and mesoscale convective systems can cause floods and landslides, causing damage to social, service, economic and financial sectors, among others, leaving the population with fewer resources and in greater vulnerability. Given this scenario, disaster prevention has relevance in the civil protection agenda, which recognizes that it is essential to establish long-range strategies and programs focused on preventing and reducing their effects, beyond only paying attention to emergencies and disasters. The objective of this work is to evaluate the performance of the WRF model for the simulation of accumulated pluvial precipitation in 24 hours in the state of Puebla, considering 768 different combinations of physical parameters, compared to rain records of weather stations for the period from June 1 to August 20, 2017. In addition, as part of the research, optimal configurations are defined to obtain the best performance of the model at local and state levels.

The aim of this work is to study the connection between sea surface temperature (SST) in the South Atlantic Ocean and the semi-permanent anticyclone. In order to do this, SST and geopotential height in 1000 hPa from the NCEP/NCAR reanalysis for the 1981-2016 period were used, with a spatial resolution of 2.5º × 2.5º and restricted to the domain 65º W-20º E, 50º S-0º to study the South Atlantic Ocean. Variability modes of the seasonal anomalies of both variables were calculated using the T mode of principal component analysis. In addition, to study the connection between both variables, the linear correlation between the temporal series of the eigenvectors of the different principal components of SST and geopotential height in 1000 hPa were calculated. Results showed that both in summer and spring, negative geopotential height anomalies to the south are associated with warmer SSTs in the central and north regions of the South Atlantic Ocean and cooler to the south. However, in autumn and winter, a lower than normal geopotential height in the southwestern region is connected to warmer SST anomalies in the south area and cooler in the center of the basin.

In the present work, two new generalized weighted methods of imputation of missing data are developed and tested using a daily rainfall series. The proposed methodology allows to fully rebuild the time series while preserving its statistical properties. Rainfall records in the state of Tabasco, Mexico, during the period 1980-2012 were used to test and evaluate the proposed methodology. The imputation of missing data in a given weather station is performed by using daily data from neighboring stations with a similar rainfall behavior. The choice of optimal parameters for the proposed formulae is based on minimizing the mean absolute error (MAE) via an evolutionary strategy (CMA-ES). The K-means method was used with the Euclidean distance in order to select the adequate neighboring weather stations. Five different methods were applied to estimate the optimal number of clusters: the elbow method, gap statistics, TraceW, Hartigan and Krzanowski-Lai indices. In addition, the structural stability of the chosen clusters was evaluated in order to demonstrate that these represent the correct data structure and are not the result of an artificial internal procedure of the grouping algorithm. Results from two different statistical tests, Friedman and Nemenyi post hoc, showed that our two new methods produce significantly and statistically better estimation when compared to existing methods in the literature. 

Two of the most important tropical cyclone parameters, which are used as a measure of their intensity, are the maximum surface wind speed (local parameter) and the pressure difference between the free atmosphere and the pressure in the center of the storm (global parameter). In this paper we reexamined the relationship between these parameters via the Euler number, which is a nondimensional parameter that relates the overall pressure difference to the dynamic pressure generated by the maximum value of the wind speed. The calculations were performed all along the track of 20 tropical cyclones using the National Hurricane Center’s extended best track database for the Atlantic basin. It was found that when the cyclone is over water, the Euler number tends to remain constant, while over land the parameter varies drastically with a tendency to grow. These results confirm that over sea, when they have reached hurricane strength, tropical cyclones evolve slowly remaining near the steady state, highlighting the importance of transient states during strong dissipation. The Euler number seems to be independent of the global warming.

Local and regional climate trends drive rates of change in coastal ecosystems. To better understand local climate, 35-year-long time series of air temperature, relative humidity and rainfall were analyzed along the reef corridor of the southwestern Gulf of Mexico. Data came from a climatological model and to assess its local performance, differences with in situ records were estimated when available. All three variables showed coherence with the record of the North Atlantic high-pressure system (also known as the Bermuda High) at similar times and periods between 4 to 8 and >10 years, evidencing the influence, at this regional scale, of El Niño Southern Oscillation (ENSO) and the Atlantic Multidecadal Oscillation (AMO). Positive and negative anomalies showed linear trends depicting an increase of warmer and moister events during a seasonal climatology at the reef corridor of the southwestern Gulf of Mexico and a relatively higher correlation (> 0.5) with the AMO mode. Return periods of extreme values varied between 5 and 10 years. In general, trends and extreme events showed similar patterns at a regional scale, but the increase in rainfall is expected to be larger near the central location of the study area. A higher frequency of extreme events could threaten local ecosystems and human populations; therefore, plans and actions at local scales of governance are needed to achieve preemptive climate adaptation. 

This study examines the impact of fossil fuels consumption, renewable energy use and industrial growth on carbon emissions in the developing economies of Latin America and the Caribbean. An industrial growth index is developed using competitive industrial indicators, and a two-step system generalized method of moments robust estimator is employed, involving a panel of 16 middle- and lower-middle-income economies for the period 1990 to 2015. The empirical results show an Inverted-U shaped relationship between economic growth and carbon emissions and confirm the existence of the environmental Kuznets curve for the region. The results indicate that industrial growth and consumption of fossil fuels are significantly contributing to carbon emissions in the region. The results highlight that, based on competitiveness in manufacturing and the transition from simple to sophisticated technologies, advance technology-based industrial growth increases the potential to produce goods competitively with lower carbon emissions. The findings suggest that such advanced industrial growth is unavoidable to attain sustainable economic growth. Thus, technological advancement and consumption of renewable energies have the potential to both meet the rising demand for goods and energy and to control carbon emissions in the developing countries of Latin America and the Caribbean.

Photosynthetically active radiation (PAR) is important in applications related to plant physiology or the carbon cycle. However, despite its importance, a global network for its measurement has not yet been established. This work consists of the revision of a series of works related to the development of empirical models for the estimation of PAR in places where it is not regularly measured, using for this purpose measurements of meteorological and radiation parameters available in weather stations. A list of the models developed, the study site, the results obtained, and the nomenclature used in each of them is made. The most common way to develop empirical estimation models is by studying spatio-temporal changes in the relationship between PAR and global solar radiation. Other estimation methods include the use of satellite-derived products such as MODIS-derived products and the use of artificial neural networks. Despite being more efficient for estimating PAR, the use of artificial neural networks is not as widespread because its use is more complex than the development of empirical models. The PAR to global solar radiation ratio reached its maximum in the summer months and the minimum in the winter months; in addition, the daily values per hour reached their maximum at sunrise and sunset, and their minimum around noon.

It is often reported that simpler models, due to their low parameter requirements, perform better than more complex models. To test this, the current study compared a simple rainfall-runoff model (IHACRES) with a complex watershed model (SWAT). Based on these two approaches, six models were developed for three climatically distinct (arid, semi-arid and semi-humid) watersheds in Iran. The coefficient of determination (R2) and the Nash-Sutcliffe model efficiency coefficient (NS) were calculated in each case. In arid, semi-arid, and semi-humid watersheds the SWAT model (R2 = 0.52, 0.68, 0.66; NS = 0.54, 0.63, 0.64, respectively) outperformed the IHACRES model (R2 = 0.37, 0.70, 0.57; NS = 0.22, 0.57, 0.56, respectively) for the same respective climate zones. Overall, SWAT performed better than IHACRES, although both models had acceptable performances in the semi-arid and semi-humid watersheds. In the arid watershed, the IHACRES model performed poorly compared to SWAT.

We present a thermodynamic model to simulate the atmospheric temperature and pressure, the regolith temperature and the polar CO2 ice caps vertical thickness and horizontal extent of Mars. This is done using the temperatures of the atmosphere and regolith for each Martian day of the year, determined from the thermodynamic equation. Assuming a CO2 atmosphere, we calculate its emission spectrum using the spectral calculator E-Trans with the HITRAN database (E-Trans/HITRAN), resulting in only a band at 15 mm and the rest transparent, through which the radiation emitted by the regolith and ice caps (considered as black bodies) goes toward space. We also include the solar radiation extinction and longwave emission of atmospheric dust. The equation calculates the energy balance between the absorbed solar radiation and the outgoing longwave radiation, incorporating also the latent heat released by CO2 condensation, the sensible heat flux from the surface to the atmosphere, the latent heat flux due to the CO2 ice sublimation and the heat exchange between the surface regolith layer and its lower layers. The atmospheric planetary scale horizontal turbulent heat transport is parameterized with an exchange coefficient, which is an order of magnitude smaller than that employed in the terrestrial troposphere. Considering a long-time average, the regolith vertical temperature profile is explicitly found using the thermal inertia including conductivity; its temperature regulation is achieved through the thermal conduction from the surface during the warm season. This stored energy goes back to the surface during the cold season. Our model simulates the seasonal variation of the polar ice caps and consequently of the surface atmospheric pressure through the CO2 mass balance between them. Finally, it is explicitly shown that the amplitude of the global seasonal pressure cycle is modulated by a local thermal-orographic effect, which increases (decreases) this amplitude in the low (high) regions, which agrees with the observations of the Viking Landers 1 and 2, and the Mars Climate Data Base. Our prediction for Hellas Planitia gives an amplification factor of up to 2.15, coinciding with other authors.

The goal of limiting the increasing global mean temperature below 2.0 and possibly 1.5 ºC, was decided in the Paris Agreement of 2015. It is therefore important to understand the climate risk and impacts associated with 1.5 and 2.0 ºC additional warming scenarios. The current study investigates the impacts of 1.5 and 2.0 ºC additional warming on wheat yield in Pakistan using a gridded modeling approach. The generated climate data by four GCMs under 1.5 and 2.0 ºC were acquired from the Half a Degree Additional Warming, Prognosis and Projected Impacts (HAPPI) scenarios group. The CERES-Wheat model was calibrated and evaluated using field data and then applied to the entire region of Pakistan. Model calibration results showed a close association between observed and simulated wheat yield with an error ranging from 0.52 to 1.36%. Climate change projections indicated that the mean temperature is expected to rise by 0.46 and 1.44 ºC in the 1.5 and 2.0 ºC additional warming scenarios in the GCMs, respectively. The spatial variations of precipitation range from –22.4 to 42.6% and 4.6 to 34.1% under the 1.5 and 2.0 ºC HAPPI scenarios, respectively. Higher precipitation was recorded in northern Pakistan as compared to central and southern Pakistan. The projected changes in temperature and precipitation will decrease the wheat yield by 3.2 and 4.7% in Punjab, 17.8% and 13.8% in Sindh province under 1.5 and 2.0 ºC additional warming, respectively. However, the wheat yield will increase by 4.7 and 13% in Khyber Pakhtunkhwa and 9.4 and 15.3% in Baluchistan under 1.5 and 2.0 ºC additional warming, respectively.

Artisanal pottery in Mexico is largely manufactured in handmade adobe kilns using scrap tires as main fuel in rural and periurban areas, making this activity one of the main sources of atmospheric pollutants. An intensive sampling campaign was conducted in a Mexican small town in order to characterize the toxic species emitted by more than 400 adobe kilns working in two scenarios: low and normal activity, as well as to determine their carcinogenic potential. PM10 concentrations ranged 50-80 µg m−3 and 77-290 µg m−3 during low and normal activity periods, while PM2.5 concentrations were 33-57 µg m−3 and 37-177 µg m−3 in the same periods. Organic carbon and elemental carbon presented concentrations around four and seven times greater, respectively, during a normal activity period than during a low activity period in both particle sizes. Quantified polycyclic aromatic hydrocarbons were twice greater during the normal activity period than during the low activity period. Carcinogenic PAHs accounted for 53-59% of total PAHs in all cases and the carcinogenic potential (B[a]Peq) in PM10 during the normal activity was 25 times higher than the European Commission recommendation, proving thereby the high risk that nearby population faces to those emissions.

Trends and forecasts of the main atmospheric pollutants (O3, SO2, NO2, CO, PM10, PM2.5, NO and NOx) are estimated by regions in the Mexico City Metropolitan Area (MCMA) with maximum daily data from 2008 to 2018. A non-parametric statistical smoothing controlled technique based on the Hodrick and Prescott filter and estimated through the Kalman filter, is used. Both point and interval estimates, as well as their respective forecasts are generated. Estimates are compared against the environmental standard for Mexico City (NADF-009-AIRE-2017), and it is evident that, in general, they are still distant from good air quality in the MCMA, as opposed to CO and NO2. The remaining pollutants have trends and forecasts that are far from the permissible limits.

Weather radar calibration is a topic of great current interest because it is useful for various hydrological applications. Several methods have been developed for adjusting the relation between reflectivity data Z and rainfall intensity R (Z/R) because droplet size distributions in different storm events are unknown and highly variable in time and space. The present study developed and tested a new space and time window-based procedure for optimal local calibration of weather radar using Z/R relations and applying it to convective and stratiform storms in the lower Grijalva river basin in Mexico. Improving rain estimates from the Sabancuy, Campeche radar is essential because it monitors this basin, which is prone to floods. The resulting estimates of the optimal power-law (Z = ARb) window-based procedure (OP) are compared with those of the default Marshall and Palmer (MP) relation using the observed rain gauge records. The appropriate window was selected using a criterion that considers factors affecting the free fall of raindrops. For most of the storms tested, metrics for the OP models showed better values than those calculated for the MP ones. The best MP performance is when using smooth calibration data, achieving similar metric results to that of the OP. The proposed observed calibration method could be useful to improve the default MP model estimates at any weather radar with similar characteristics to the ones analyzed in this work. The resulting Z/R relations could improve precipitation radar estimates for hydrologic model inputs.

The Nevado de Toluca weather station (4283 masl, 19 ºN) has recorded meteorological data for over half a century, and this combination of elevation and duration provides a rare opportunity to study climate trends in a tropical high-mountain environment. The climatic variability during the period 1965-2015 at the Nevado de Toluca volcano was analyzed. Nine standard climate indices for temperature and seven for precipitation were calculated from daily data from its weather station. The results, with a high level of statistical significance, show an increase in the number of days with night frost and cold periods; likewise, results indicate an increase in the diurnal thermal oscillation. Total accumulated precipitation shows an increasing tendency over time, although the periods with precipitation are increasingly isolated. This suggests that seasonal snow on the summit of the volcano will be increasingly isolated but, at the same time, the snowpack will persist longer. This work is expected to serve as a reference for other high-mountain tropical environmental studies, where air temperature and precipitation are crucial issues.