Impact of Forecast Stability on Navigational Contrail Avoidance

Abstract

MMitigating contrail-induced warming by re-routing flights around contrail-forming regions requires accurate and stable forecasts of the state of the upper troposphere and lower stratosphere. Forecast stability (i.e., consistency between forecast cycles with different lead times) is particularly important for "pre-tactical" contrail avoidance strategies that adjust routes based on forecasts with lead times as long as 24-48 hours. However, no study to date has systematically quantified the degree to which forecast stability limits the effectiveness of pre-tactical avoidance. This study addresses this gap by comparing contrail forecasts generated using ECMWF HRES weather forecasts with lead times up to 48 hours to contrail hindcasts generated based on ECMWF ERA5 reanalysis. An analysis of forecast errors show low pointwise consistency between persistent-contrail-forming regions in forecasts and reanalysis, with pointwise error rates similar to those found in previous comparisons of contrail-forming regions in reanalysis and reality. However, we also show that spatial errors in the locations of contrail-forming regions are relatively small, both when forecasts are compared to reanalysis and when reanalysis is compared to in-situ measurements. Finally, we show that designing a trajectory optimizer to take advantage of relatively small spatial errors allows flight trajectory optimizations based on contrail forecasts to reduce contrail climate forcing evaluated based on reanalysis by 80-90% at the 8-24 hour lead times most relevant to flight planning, with fuel penalties under 0.4%. Our results show that forecasts with lead times relevant to flight planning are stable enough to be used for pre-tactical contrail avoidance.

Figures (11)

• Figure 1: Two example optimized flight trajectories, showing for each a cost-optimal trajectory (dashed line) and a contrail-optimal trajectory (solid line). The trajectory on the left-hand side reduces contrail EF by $1.2 \times 10^{15} \,\mathrm{J}$ ($340,000 \,\mathrm{kg}$ CO$_2$eAGWP100), with an additional fuel burn of $2,233 \,\mathrm{kg}$ ($+4.7\%$). The trajectory on the right-hand side saves $6.3 \times 10^{13} \,\mathrm{J}$ ($18,000 \,\mathrm{kg}$ CO$_2$eAGWP100) of contrail EF, with a fuel penalty of $200 \,\mathrm{kg}$ ($+2.4\%$).
• Figure 2: Cumulative contrail EF produced by simulated warming contrails with EF per unit flight distance below a threshold (right side of vertical dashed line) and by simulated cooling contrails with EF per unit flight distance above a threshold (left side of vertical dashed line). EF values shown in this figure were obtained from gridded CoCiP calculations using ERA5 met data interpolated to waypoints along cost-optimal trajectories computed using ERA5 meteorology.
• Figure 3: Equitable threat scores for HRES vs. ERA5 ISSRs (blue), HRES vs. ERA5 high-EF regions (red), and ERA5 vs. IAGOS ISSRs (black). ETS values for HRES vs. ERA5 are shown as a function of forecast lead time, with error bars representing 95% confidence intervals. The 95% confidence interval for the ERA5-IAGOS ETS is shown as shaded gray around the dashed black line. High-EF regions are defined as regions with contrail warming per unit flight distance greater than $10^7$ J/m and are a subset of ISSRs.
• Figure 4: (a) Cumulative distribution functions (CDFs) measuring the distance between HRES vs. ERA5 ISSRs (blue) and ERA5 vs IAGOS ISSRs (black), henceforth referred to as proximity distributions. (b) Proximity distributions for HRES vs ERA5 high-EF regions. (c) Fraction of waypoints in ERA5 ISSRs that are within an hour's flight time of an HRES ISSR (blue) and of waypoints in ERA5 high-EF regions that are within an hour's flight time of an HRES high-EF region (red), binned by forecast lead time. High EF regions are defined as regions with EF per unit flight distance greater than $10^7$ J m$^{-1}$. Flights with a duration less than four hours are excluded to avoid large differences in the average duration of optimized trajectories and IAGOS flights. In panel (c), "HRES positives" refer to ISSRs and high-EF regions for blue and red points, respectively.
• Figure 5: (a) pointwise agreement between high-EF regions from ERA5 reanalysis and the 36-hour HRES forecast at FL320 at 6Z on 01 March 2019. True positives are blue, false positives are orange, false negatives are red, and true negatives are white. The black lines show the lengths of 90 and 10 minute flight segments assuming a groundspeed of $250 \,\mathrm{m/s}$. (b) Illustration of how trajectory optimization aims to accommodate errors in spatial locations of contrail-forming regions in forecasts and reanalysis by constraining minimum segment lengths between altitude changes. A hypothetical contrail-optimal trajectory (black) has been re-routed from a cost-optimal trajectory (gray) to avoid the regions where forecast EF was high. Due to the constraint on the minimum segment length, the trajectory also avoided a region where forecast EF was low but reanalysis EF was high.